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HARE
(2023)
Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE’s multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.
Mussel-inspired multifunctional coating for bacterial infection prevention and osteogenic induction
(2021)
Bacterial infection and osteogenic integration are the two main problems that cause severe complications after surgeries. In this study, the antibacterial and osteogenic properties were simultaneously introduced in biomaterials, where copper nanoparticles (CuNPs) were generated by in situ reductions of Cu ions into a mussel-inspired hyperbranched polyglycerol (MI-hPG) coating via a simple dip-coating method. This hyperbranched polyglycerol with 10 % catechol groups' modification presents excellent antifouling property, which could effectively reduce bacteria adhesion on the surface. In this work, polycaprolactone (PCL) electrospun fiber membrane was selected as the substrate, which is commonly used in biomedical implants in bone regeneration and cardiovascular stents because of its good biocompatibility and easy post-modification. The as-fabricated CuNPs-incorporated PCL membrane [PCL-(MI-hPG)-CuNPs] was confirmed with effective antibacterial performance via in vitro antibacterial tests against Staphylococcus aureus (S. aureus), Escherichia coli (E. coli), and multi-resistant E. coli. In addition, the in vitro results demonstrated that osteogenic property of PCL-(MI-hPG)-CuNPs was realized by upregulating the osteoblast-related gene expressions and protein activity. This study shows that antibacterial and osteogenic properties can be balanced in a surface coating by introducing CuNPs.
In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer. The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements.
In recent years, many efforts have been made to apply image processing techniques for plant leaf identification. However, categorizing leaf images at the cultivar/variety level, because of the very low inter-class variability, is still a challenging task. In this research, we propose an automatic discriminative method based on convolutional neural networks (CNNs) for classifying 12 different cultivars of common beans that belong to three various species. We show that employing advanced loss functions, such as Additive Angular Margin Loss and Large Margin Cosine Loss, instead of the standard softmax loss function for the classification can yield better discrimination between classes and thereby mitigate the problem of low inter-class variability. The method was evaluated by classifying species (level I), cultivars from the same species (level II), and cultivars from different species (level III), based on images from the leaf foreside and backside. The results indicate that the performance of the classification algorithm on the leaf backside image dataset is superior. The maximum mean classification accuracies of 95.86, 91.37 and 86.87% were obtained at the levels I, II and III, respectively. The proposed method outperforms the previous relevant works and provides a reliable approach for plant cultivars identification.
Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep reinforcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensorand process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
Sharing marketplaces emerged as the new Holy Grail of value creation by enabling exchanges between strangers. Identity reveal, encouraged by platforms, cuts both ways: While inducing pre-transaction confidence, it is suspected of backfiring on the information senders with its discriminative potential. This study employs a discrete choice experiment to explore the role of names as signifiers of discriminative peculiarities and the importance of accompanying cues in peer choices of a ridesharing offer. We quantify users' preferences for quality signals in monetary terms and evidence comparative disadvantage of Middle Eastern descent male names for drivers and co-travelers. It translates into a lower willingness to accept and pay for an offer. Market simulations confirm the robustness of the findings. Further, we discover that females are choosier and include more signifiers of involuntary personal attributes in their decision-making. Price discounts and positive information only partly compensate for the initial disadvantage, and identity concealment is perceived negatively.
Sharing marketplaces emerged as the new Holy Grail of value creation by enabling exchanges between strangers. Identity reveal, encouraged by platforms, cuts both ways: While inducing pre-transaction confidence, it is suspected of backfiring on the information senders with its discriminative potential. This study employs a discrete choice experiment to explore the role of names as signifiers of discriminative peculiarities and the importance of accompanying cues in peer choices of a ridesharing offer. We quantify users' preferences for quality signals in monetary terms and evidence comparative disadvantage of Middle Eastern descent male names for drivers and co-travelers. It translates into a lower willingness to accept and pay for an offer. Market simulations confirm the robustness of the findings. Further, we discover that females are choosier and include more signifiers of involuntary personal attributes in their decision-making. Price discounts and positive information only partly compensate for the initial disadvantage, and identity concealment is perceived negatively.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
This study investigates the use of pulse stretching (skew-sized) inverters for monitoring the variation of count rate and linear energy transfer (LET) of energetic particles. The basic particle detector is a cascade of two pulse stretching inverters, and the required sensing area is obtained by connecting up to 12 two-inverter cells in parallel and employing the required number of parallel arrays. The incident particles are detected as single-event transients (SETs), whereby the SET count rate denotes the particle count rate, while the SET pulsewidth distribution depicts the LET variations. The advantage of the proposed solution is the possibility to sense the LET variations using fully digital processing logic. SPICE simulations conducted on IHP's 130-nm CMOS technology have shown that the SET pulsewidth varies by approximately 550 ps over the LET range from 1 to 100 MeV center dot cm(2) center dot mg(-1). The proposed detector is intended for triggering the fault-tolerant mechanisms within a self-adaptive multiprocessing system employed in space. It can be implemented as a standalone detector or integrated in the same chip with the target system.
Cirrus is the only cloud type capable of inducing daytime cooling or heating at the top of the atmosphere (TOA) and the sign of its radiative effect highly depends on its optical depth. However, the investigation of its geometrical and optical properties over the Arctic is limited. In this work the long-term properties of cirrus clouds are explored for the first time over an Arctic site (Ny-Alesund, Svalbard) using lidar and radiosonde measurements from 2011 to 2020. The optical properties were quality assured, taking into account the effects of specular reflections and multiple-scattering. Cirrus clouds were generally associated with colder and calmer wind conditions compared to the 2011-2020 climatology. However, the dependence of cirrus properties on temperature and wind speed was not strong. Even though the seasonal cycle was not pronounced, the winter-time cirrus appeared under lower temperatures and stronger wind conditions. Moreover, in winter, geometrically- and optically-thicker cirrus were found and their ice particles tended to be more spherical. The majority of cirrus was associated with westerly flow and westerly cirrus tended to be geometrically-thicker. Overall, optically-thinner layers tended to comprise smaller and less spherical ice crystals, most likely due to reduced water vapor deposition on the particle surface. Compared to lower latitudes, the cirrus layers over Ny-Alesund were more absorbing in the visible spectral region and they consisted of more spherical ice particles.
Krisenvorstellungen
(2022)
Der Beitrag stellt zentrale Ergebnisse der qualitativen Untersuchung zum Thema „Gesellschaftliche Herausforderungen im sozialen und im schulischen Raum“ dar. Dabei wird zunächst nur der erste Teil und damit das Erfahrungswissen im sozialen Raum beleuchtet. Neben einer kurzen Darstellung des theoretischen und methodischen Zugangs werden unterschiedliche Krisenverständnisse von Lehrer/-innen herausgestellt und auf sozialwissenschaftliche Erkenntnisse zurückgeführt. Der Rekurs auf die Krise(n) wird als Zugang genutzt, um gesellschaftliche He-rausforderungen zu identifizieren und Einschätzungen zu explizieren. In einem zweiten Schritt werden zwei Typen präsentiert, durch die exemplarisch konträre Vorstellungen zu unterschiedlichen gesellschaftlichen Herausforderungen und Krisen herausgestellt werden können. Durch die zwei Typen „progressive“ und „konservative Kritiker/-innen“ kann ein Spannungsfeld aufgemacht werden, auf dem die untersuchten Fälle verortet werden. Ziel ist es, Erfahrungswissen und die gesellschaftlichen Sichtweisen wie auch politischen Überzeugungen sichtbar und vergleichbar werden zu lassen. Diese bilden die Grundlage, um anschließend zu untersuchen, wie sich Vorstellungen und Überzeugungen auch im schulischen Raum wiederfinden lassen. Ein erster Einblick wird am Ende des Beitrags durch die Darstellung eines exemplarischen Falls gewährt.
Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40-0.56 m yr(-1) for lake sizes of 0.10 ha-23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations.
Thermally stable photoswitches that are driven with low-energy light are rare, yet crucial for extending the applicability of photoresponsive molecules and materials towards, e.g., living systems. Combined ortho-fluorination and -amination couples high visible light absorptivity of o-aminoazobenzenes with the extraordinary bistability of o-fluoroazobenzenes. Herein, we report a library of easily accessible o-aminofluoroazobenzenes and establish structure-property relationships regarding spectral qualities, visible light isomerization efficiency and thermal stability of the cis-isomer with respect to the degree of o-substitution and choice of amino substituent. We rationalize the experimental results with quantum chemical calculations, revealing the nature of low-lying excited states and providing insight into thermal isomerization. The synthesized azobenzenes absorb at up to 600 nm and their thermal cis-lifetimes range from milliseconds to months. The most unique example can be driven from trans to cis with any wavelength from UV up to 595 nm, while still exhibiting a thermal cis-lifetime of 81 days. <br /> [GRAPHICS] <br /> .
Frequency-domain electromagnetic (FDEM) data are commonly inverted to characterize subsurface geoelectrical properties using smoothness constraints in 1D inversion schemes assuming a layered medium.
Smoothness constraints are suitable for imaging gradual transitions of subsurface geoelectrical properties caused, for example, by varying sand, clay, or fluid content. However, such inversion approaches are limited in characterizing sharp interfaces. Alternative regularizations based on the minimum gradient support (MGS) stabilizers can, instead, be used to promote results with different levels of smoothness/sharpness selected by simply acting on the so-called focusing parameter.
The MGS regularization has been implemented for different kinds of geophysical data inversion strategies. However, concerning FDEM data, the MGS regularization has only been implemented for vertically constrained inversion (VCI) approaches but not for laterally constrained inversion (LCI) approaches.
We present a novel LCI approach for FDEM data using the MGS regularization for the vertical and lateral direction. Using synthetic and field data examples, we demonstrate that our approach can efficiently and automatically provide a set of model solutions characterized by different levels of sharpness and variable lateral consistencies.
In terms of data misfit, the obtained set of solutions contains equivalent models allowing us also to investigate the non-uniqueness of FDEM data inversion.
Simple and robust
(2021)
A spectrum of 7562 publications on Molecularly Imprinted Polymers (MIPs) has been presented in literature within the last ten years (Scopus, September 7, 2020). Around 10 % of the papers published on MIPs describe the recognition of proteins. The straightforward synthesis of MIPs is a significant advantage as compared with the preparation of enzymes or antibodies. MIPs have been synthesized from only one up to six functional monomers while proteins are made up of 20 natural amino acids. Furthermore, they can be synthesized against structures of low immunogenicity and allow multi-analyte measurements via multi-target synthesis. Electrochemical methods allow simple polymer synthesis, removal of the template and readout. Among the different sensor configurations electrochemical MIP-sensors provide the broadest spectrum of protein analytes. The sensitivity of MIP-sensors is sufficiently high for biomarkers in the sub-nanomolar region, nevertheless the cross-reactivity of highly abundant proteins in human serum is still a challenge. MIPs for proteins offer innovative tools not only for clinical and environmental analysis, but also for bioimaging, therapy and protein engineering.
The nanoscale combination of a conductive carbon and a carbon-based material with abundant heteroatoms for battery electrodes is a method to overcome the limitation that the latter has high affinity to alkali metal ions but low electronic conductivity. The synthetic protocol and the individual ratios and structures are important aspects influencing the properties of such multifunctional compounds. Their interplay is, herein, investigated by infiltration of a porous ZnO-templated carbon (ZTC) with nitrogen-rich carbon obtained by condensation of hexaazatriphenylene-hexacarbonitrile (HAT-CN) at 550-1000 degrees C. The density of lithiophilic sites can be controlled by HAT-CN content and condensation temperature. Lithium storage properties are significantly improved in comparison with those of the individual compounds and their physical mixtures. Depending on the uniformity of the formed composite, loading ratio and condensation temperature have different influence. Most stable operation at high capacity per used monomer is achieved with a slowly dried composite with an HAT-CN:ZTC mass ratio of 4:1, condensed at 550 degrees C, providing more than 400 mAh g(-1) discharge capacity at 0.1 A g(-1) and a capacity retention of 72% after 100 cycles of operation at 0.5 A g(-1) due to the homogeneity of the composite and high content of lithiophilic sites.
ControlCenter 4.0
(2021)
Droughts in São Paulo
(2023)
Literature has suggested that droughts and societies are mutually shaped and, therefore, both require a better understanding of their coevolution on risk reduction and water adaptation. Although the Sao Paulo Metropolitan Region drew attention because of the 2013-2015 drought, this was not the first event. This paper revisits this event and the 1985-1986 drought to compare the evolution of drought risk management aspects. Documents and hydrological records are analyzed to evaluate the hazard intensity, preparedness, exposure, vulnerability, responses, and mitigation aspects of both events. Although the hazard intensity and exposure of the latter event were larger than the former one, the policy implementation delay and the dependency of service areas in a single reservoir exposed the region to higher vulnerability. In addition to the structural and non-structural tools implemented just after the events, this work raises the possibility of rainwater reuse for reducing the stress in reservoirs.
Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building
(2021)
In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87% of time, on average, by 0.53 degrees C compared to the current deviations.
A method for the fabrication of well-defined metallic nanostructures is presented here in a simple and straightforward fashion. As an alternative to lithographic techniques, this routine employs microcontact printing utilizing wrinkled stamps, which are prepared from polydimethylsiloxane (PDMS), and includes the formation of hydrophobic stripe patterns on a substrate via the transfer of oligomeric PDMS. Subsequent backfilling of the interspaces between these stripes with a hydroxyl-functional poly(2-vinyl pyridine) then provides the basic pattern for the deposition of citrate-stabilized gold nanoparticles promoted by electrostatic interaction. The resulting metallic nanostripes can be further customized by peeling off particles in a second microcontact printing step, which employs poly(ethylene imine) surface-decorated wrinkled stamps, to form nanolattices. Due to the independent adjustability of the period dimensions of the wrinkled stamps and stamp orientation with respect to the substrate, particle arrays on the (sub)micro-scale with various kinds of geometries are accessible in a straightforward fashion. This work provides an alternative, cost-effective, and scalable surface-patterning technique to fabricate nanolattice structures applicable to multiple types of functional nanoparticles. Being a top-down method, this process could be readily implemented into, e.g., the fabrication of optical and sensing devices on a large scale.
Photoluminescence spectroscopy is a widely applied characterization technique for semiconductor materials in general and halide perovskite solar cell materials in particular. It can give direct information on the recombination kinetics and processes as well as the internal electrochemical potential of free charge carriers in single semiconductor layers, layer stacks with transport layers, and complete solar cells. The correct evaluation and interpretation of photoluminescence requires the consideration of proper excitation conditions, calibration and application of the appropriate approximations to the rather complex theory, which includes radiative recombination, non-radiative recombination, interface recombination, charge transfer, and photon recycling. In this article, an overview is given of the theory and application to specific halide perovskite compositions, illustrating the variables that should be considered when applying photoluminescence analysis in these materials.
Photoluminescence spectroscopy is a widely applied characterization technique for semiconductor materials in general and halide perovskite solar cell materials in particular. It can give direct information on the recombination kinetics and processes as well as the internal electrochemical potential of free charge carriers in single semiconductor layers, layer stacks with transport layers, and complete solar cells. The correct evaluation and interpretation of photoluminescence requires the consideration of proper excitation conditions, calibration and application of the appropriate approximations to the rather complex theory, which includes radiative recombination, non-radiative recombination, interface recombination, charge transfer, and photon recycling. In this article, an overview is given of the theory and application to specific halide perovskite compositions, illustrating the variables that should be considered when applying photoluminescence analysis in these materials.
We search for homovalent alternatives for A, B, and X-ions in ABX(3) type inorganic halide perovskites suitable for tandem solar cell applications. We replace the conventional A-site organic cation CH3NH3, by 3 inorganic cations, Cs, K, and Rb, and the B site consists of metals; Cd, Hg, Ge, Pb, and Sn This work is built on our previous high throughput screening of hybrid perovskite materials (Kar et al 2018 J. Chem. Phys. 149, 214701). By performing a systematic screening study using Density Functional Theory (DFT) methods, we found 11 suitable candidates; 2 Cs-based, 3 K-based and 6 Rb-based that are suitable for tandem solar cell applications.
The evaluation of how (human) individuals perceive robots is a central issue to better understand human-robot interaction (HRI). On this topic, promising proposals have emerged. However, present tools are not able to assess a sufficient part of the composite psychological dimensions involved in the evaluation of HRI. Indeed, the percentage of variance explained is often under the recommended threshold for a construct to be valid. In this article, we consolidate the lessons learned from three different studies and propose a further developed questionnaire based on a multicomponent approach of anthropomorphism by adding traits from psychosocial theory about the perception of others and the attribution and deprivation of human characteristics: the de-humanization theory. Among these characteristics, the attribution of agency is of main interest in the field of social robotics as it has been argued that robots could be considered as intentional agents. Factor analyses reveal a four sub-dimensions scale including Sociability, Agency, Animacy, and the Disturbance. We discuss the implication(s) of these dimensions on future perception of and attitudes towards robots.
Already successfully used products or designs, past projects or our own experiences can be the basis for the development of new products. As reference products or existing knowledge, it is reused in the development process and across generations of products. Since further, products are developed in cooperation, the development of new product generations is characterized by knowledge-intensive processes in which information and knowledge are exchanged between different kinds of knowledge carriers. The particular knowledge transfer here describes the identification of knowledge, its transmission from the knowledge carrier to the knowledge receiver, and its application by the knowledge receiver, which includes embodied knowledge of physical products. Initial empirical findings of the quantitative effects regarding the speed of knowledge transfers already have been examined. However, the factors influencing the quality of knowledge transfer to increase the efficiency and effectiveness of knowledge transfer in product development have not yet been examined empirically. Therefore, this paper prepares an experimental setting for the empirical investigation of the quality of knowledge transfers.
Technological developments such as Cloud Computing, the Internet of Things, Big Data and Artificial Intelligence continue to drive the digital transformation of business and society. With the advent of platform-based ecosystems and their potential to address complex challenges, there is a trend towards greater interconnectedness between different stakeholders to co-create services based on the provision and use of data. While previous research on digital transformation mainly focused on digital transformation within organizations, it is of growing importance to understand the implications for digital transformation on different layers (e.g., interorganizational cooperation and platform ecosystems). In particular, the conceptualization and implications of public data spaces and related ecosystems provide promising research opportunities. This special issue contains five papers on the topic of digital transformation and, with the editorial, further contributes by providing an initial conceptualization of public data spaces' potential to foster innovative progress and digital transformation from a management perspective.
Purpose
Given inconsistent results in prior studies, this paper applies the dual process theory to investigate what social media messages yield audience engagement during a political event. It tests how affective cues (emotional valence, intensity and collective self-representation) and cognitive cues (insight, causation, certainty and discrepancy) contribute to public engagement.
Design/methodology/approach
The authors created a dataset of more than three million tweets during the 2020 United States (US) presidential elections. Affective and cognitive cues were assessed via sentiment analysis. The hypotheses were tested in negative binomial regressions. The authors also scrutinized a subsample of far-famed Twitter users. The final dataset, scraping code, preprocessing and analysis are available in an open repository.
Findings
The authors found the prominence of both affective and cognitive cues. For the overall sample, negativity bias was registered, and the tweet’s emotionality was negatively related to engagement. In contrast, in the sub-sample of tweets from famous users, emotionally charged content produced higher engagement. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with many followers. Collective self-representation (“we-talk”) is consistently associated with more likes, comments and retweets in the overall sample and subsamples.
Originality/value
The authors expand the dominating one-sided perspective to social media message processing focused on the peripheral route and hence affective cues. Leaning on the dual process theory, the authors shed light on the effectiveness of both affective (peripheral route) and cognitive (central route) cues on information appeal and dissemination on Twitter during a political event. The popularity of the tweet’s author moderates these relationships.
Online businesses are increasingly relying on targeted advertisements as a revenue stream, which might lead to privacy concerns and hinder product adoption. Therefore, it is crucial for online companies to understand which types of targeted advertisements consumers will accept. In recent years, users have been increasingly targeted by political advertisements, which has caused adverse reactions in media and society. Nonetheless, few studies experimentally investigate user privacy concerns and their role in acceptance decisions in response to targeted political advertisements. To fill this gap, we explore the magnitude of privacy concerns towards targeted political ads compared to “traditional” targeting in the product context. Surprisingly, we find no notable differences in privacy concerns between these data use purposes. In the next step, user preferences over ad types are elicited with the help of a discrete choice experiment in the mobile app adoption context. Our findings suggest that while targeted political advertising is somewhat less desirable than targeted product advertising, the odds of choosing an app are statistically insignificant between two data use purposes. Together, these results contribute to a better understanding of users’ privacy concerns and preferences in the context of targeted political advertising online.
Additive manufacturing (AM) processes enable the production of metal structures with exceptional design freedom, of which laser powder bed fusion (PBF-LB) is one of the most common. In this process, a laser melts a bed of loose feedstock powder particles layer-by-layer to build a structure with the desired geometry. During fabrication, the repeated melting and rapid, directional solidification create large temperature gradients that generate large thermal stress. This thermal stress can itself lead to cracking or delamination during fabrication. More often, large residual stresses remain in the final part as a footprint of the thermal stress. This residual stress can cause premature distortion or even failure of the part in service. Hence, knowledge of the residual stress field is critical for both process optimization and structural integrity.
Diffraction-based techniques allow the non-destructive characterization of the residual stress fields. However, such methods require a good knowledge of the material of interest, as certain assumptions must be made to accurately determine residual stress. First, the measured lattice plane spacings must be converted to lattice strains with the knowledge of a strain-free material state. Second, the measured lattice strains must be related to the macroscopic stress using Hooke's law, which requires knowledge of the stiffness of the material. Since most crystal structures exhibit anisotropic material behavior, the elastic behavior is specific to each lattice plane of the single crystal. Thus, the use of individual lattice planes in monochromatic diffraction residual stress analysis requires knowledge of the lattice plane-specific elastic properties. In addition, knowledge of the microstructure of the material is required for a reliable assessment of residual stress.
This work presents a toolbox for reliable diffraction-based residual stress analysis. This is presented for a nickel-based superalloy produced by PBF-LB. First, this work reviews the existing literature in the field of residual stress analysis of laser-based AM using diffraction-based techniques. Second, the elastic and plastic anisotropy of the nickel-based superalloy Inconel 718 produced by PBF-LB is studied using in situ energy dispersive synchrotron X-ray and neutron diffraction techniques. These experiments are complemented by ex situ material characterization techniques. These methods establish the relationship between the microstructure and texture of the material and its elastic and plastic anisotropy. Finally, surface, sub-surface, and bulk residual stress are determined using a texture-based approach. Uncertainties of different methods for obtaining stress-free reference values are discussed.
The tensile behavior in the as-built condition is shown to be controlled by texture and cellular sub-grain structure, while in the heat-treated condition the precipitation of strengthening phases and grain morphology dictate the behavior. In fact, the results of this thesis show that the diffraction elastic constants depend on the underlying microstructure, including texture and grain morphology. For columnar microstructures in both as-built and heat-treated conditions, the diffraction elastic constants are best described by the Reuss iso-stress model. Furthermore, the low accumulation of intergranular strains during deformation demonstrates the robustness of using the 311 reflection for the diffraction-based residual stress analysis with columnar textured microstructures. The differences between texture-based and quasi-isotropic approaches for the residual stress analysis are shown to be insignificant in the observed case. However, the analysis of the sub-surface residual stress distributions show, that different scanning strategies result in a change in the orientation of the residual stress tensor. Furthermore, the location of the critical sub-surface tensile residual stress is related to the surface roughness and the microstructure. Finally, recommendations are given for the diffraction-based determination and evaluation of residual stress in textured additively manufactured alloys.
Energy models are used to inform and support decisions within the transition to climate neutrality. In recent years, such models have been criticised for being overly techno-centred and ignoring environmental and social factors of the energy transition. Here, we explore and illustrate the impact of ignoring such factors by comparing model results to model user needs and real-world observations. We firstly identify concrete user needs for better representation of environmental and social factors in energy modelling via interviews, a survey and a workshop. Secondly, we explore and illustrate the effects of omitting non-techno-economic factors in modelling by contrasting policy-targeted scenarios with reality in four EU case study examples. We show that by neglecting environmental and social factors, models risk generating overly optimistic and potentially misleading results, for example by suggesting transition speeds far exceeding any speeds observed, or pathways facing hard-to-overcome resource constraints. As such, modelled energy transition pathways that ignore such factors may be neither desirable nor feasible from an environmental and social perspective, and scenarios may be irrelevant in practice. Finally, we discuss a sample of recent energy modelling innovations and call for continued and increased efforts for improved approaches that better represent environmental and social factors in energy modelling and increase the relevance of energy models for informing policymaking.
The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments of wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, constraints due to regulation or public opposition, expert and stakeholder workshops, willingness to pay/accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.
Beyond technology
(2022)
This article enriches the existing literature on the importance and role of the social sciences and humanities (SSH) in renewable energy sources research by providing a novel approach to instigating the future research agenda in this field. Employing a series of in-depth interviews, deliberative focus group workshops and a systematic horizon scanning process, which utilised the expert knowledge of 85 researchers from the field with diverse disciplinary backgrounds and expertise, the paper develops a set of 100 priority questions for future research within SSH scholarship on renewable energy sources. These questions were aggregated into four main directions: (i) deep transformations and connections to the broader economic system (i.e. radical ways of (re)arranging socio-technical, political and economic relations), (ii) cultural and geographical diversity (i.e. contextual cultural, historical, political and socio-economic factors influencing citizen support for energy transitions), (iii) complexifying energy governance (i.e. understanding energy systems from a systems dynamics perspective) and (iv) shifting from instrumental acceptance to value-based objectives (i.e. public support for energy transitions as a normative notion linked to trust-building and citizen engagement). While this agenda is not intended to be—and cannot be—exhaustive or exclusive, we argue that it advances the understanding of SSH research on renewable energy sources and may have important value in the prioritisation of SSH themes needed to enrich dialogues between policymakers, funding institutions and researchers. SSH scholarship should not be treated as instrumental to other research on renewable energy but as intrinsic and of the same hierarchical importance.
There are considerable differences in the pace and underlying motivations of the energy transition in the different geographical contexts across Europe. The European Union's commitment to climate neutrality by 2050 requires a better understanding of the energy transition in different contexts and scales to improve cooperation of involved actors. In this article, we identify critical issues and challenges of the European energy transition as perceived by stakeholders and investigate how these perceptions vary across geographical contexts. To do so, we couple a policy document analysis with research based on stakeholder engagement activities in three different scales, national (Greece), regional (Nordic Region) and continental scale (European Union). Our findings show that stakeholder perspectives on the energy transition depend on contextual factors underlying the need for policies sensitive to the different transition issues and challenges in European regions. They also reveal cross-cutting issues and challenges among the three case studies, which could lead to further improvement of the cross-country collaboration to foster the European energy transition.
To achieve the European Union's target for climate neutrality by 2050 reduced energy demand will make the transition process faster and cheaper. The role of policies that support energy efficiency measures and demand-side management practices will be critical and to ensure that energy demand models are relevant to policymakers and other end-users, understanding how to further improve the models and whether they are tailored to user needs to support efficient decision-making processes is crucial. So far though, no scientific studies have examined the key user needs for energy demand modelling in the context of the climate neutrality targets. In this article we address this gap using a multi-method approach based on empirical and desk research. Through survey and stakeholder meetings and workshops we identify user needs of different stakeholder groups, and we highlight the direction in which energy demand models need to be improved to be relevant to their users. Through a detailed review of existing energy demand models, we provide a full understanding of the key characteristics and capabilities of existing tools, and we identify their limitations and gaps. Our findings show that classical demand-related questions remain important to model users, while most of the existing models can answer these questions. Furthermore, we show that some of the user needs related to sectoral demand modelling, dictated by the latest policy developments, are under-researched and are not addressed by existing tools.
Renewable energy changes the geopolitics of energy: whereas access to fossil fuel resources were key in the past, control over technology and industry will be key in the future. Consequently, different scholars have predicted that a growing focus on renewables will increase or decrease conflict in the energy sector, with no consensus on which is most likely. Here, we investigate the degree of conflict in renewable energy technology (RET) trade by analyzing data on 7041 trade conflicts 1995–2020, guided by two sets of theory-driven hypotheses. We show that RET trade is associated with more, longer, and more intense trade conflicts than other trade conflicts for 1995–2016. This supports the neorealist, geo-economic view of countries being willing to risk conflict to increase their share of a market rather than avoiding conflicts to increase the overall market size. It also contradicts the view that renewables will reduce conflict: at least in the past and regarding trade, it has increased rather than decreased conflict. For 2017–2020, this trend is reversed and RET trade became significantly less conflictive than other trade. Our findings imply that improved conflict-resolution institutions for RET are needed. We also suggest establishing specific institutions to govern trade in immature technologies.
Background Boredom during learning activities has the potential of impeding attention, motivation, learning and eventually achievement. Yet, research focusing on its possible antecedents seems to have received less attention especially within the physics domain. Based on assumptions of the Control Value Theory of Achievement Emotions (CVTAE), this study aimed at examining gender differences and structural relationships between students' reported perceived teacher autonomy support (PTAS), cognitive appraisals (self-efficacy and task value) and learning-related boredom in physics. A sample of 375 (56% females) randomly selected 9(th) grade students (mean age = 15.03 years; SD = 1.02) from five secondary schools in Masaka district of Uganda took part in the study. Results Data collected from students' self-reports using standardised instruments revealed that higher levels of PTAS, self-efficacy, and task value were significantly associated with lower levels of boredom during physics learning. Females reported significantly greater task value for learning physics than the males. Self-efficacy (beta = - .10, p < .05) and task value (beta = - .09, p < .01) partially mediated the relationship between PTAS and boredom. PTAS showed significant direct negative contributions to boredom (beta = - .34, p < .001). Conclusion These findings provide support for theory and practice about the importance of promoting autonomy among students by adjusting instructional behaviours among teachers of physics. Teacher autonomy supportive behaviours influence formation of students' beliefs about ability, subjective value and learning-related boredom in physics. Implications and suggestions for further research are also discussed in this paper.
In this study we analyze the storm-time evolution of equatorial electron pitch angle distributions (PADs) in the outer radiation belt region using observations from the Magnetic Electron Ion Spectrometer (MagEIS) instrument aboard the Van Allen Probes in 2012-2019. The PADs are approximated using a sum of the first, third and fifth sine harmonics. Different combinations of the respective coefficients refer to the main PAD shapes within the outer radiation belt, namely the pancake, flat-top, butterfly and cap PADs. We conduct a superposed epoch analysis of 129 geomagnetic storms and analyze the PAD evolution for day and night MLT sectors. PAD shapes exhibit a strong energy-dependent response. At energies of tens of keV, the PADs exhibit little variation throughout geomagnetic storms. Cap PADs are mainly observed at energies < 300 keV, and their extent in L shrinks with increasing energy. The cap distributions transform into the pancake PADs around the main phase of the storm on the nightside, and then come back to their original shapes during the recovery phase. At higher energies on the dayside, the PADs are mainly pancake during pre-storm conditions and become more anisotropic during the main phase. The quiet-time butterfly PADs can be observed on the nightside at L> 5.6. During the main phase, butterfly PADs have stronger 90 degrees-minima and can be observed at lower L-shells (down to L = 5), then transitioning into flat-top PADs at L similar to 4.5 - 5 and pancake PADs at L < 4.5. The resulting PAD coefficients for different energies, locations and storm epochs can be used to test the wave models and physics-based radiation belt codes in terms of pitch angle distributions.
Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops
(2021)
Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h(-1) area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields.
In clinical practice, only a few reliable measurement instruments are available for monitoring knee joint rehabilitation. Advances to replace motion capturing with sensor data measurement have been made in the last years. Thus, a systematic review of the literature was performed, focusing on the implementation, diagnostic accuracy, and facilitators and barriers of integrating wearable sensor technology in clinical practices based on a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. For critical appraisal, the COSMIN Risk of Bias tool for reliability and measurement of error was used. PUBMED, Prospero, Cochrane database, and EMBASE were searched for eligible studies. Six studies reporting reliability aspects in using wearable sensor technology at any point after knee surgery in humans were included. All studies reported excellent results with high reliability coefficients, high limits of agreement, or a few detectable errors. They used different or partly inappropriate methods for estimating reliability or missed reporting essential information. Therefore, a moderate risk of bias must be considered. Further quality criterion studies in clinical settings are needed to synthesize the evidence for providing transparent recommendations for the clinical use of wearable movement sensors in knee joint rehabilitation.
Averting today's loss of biodiversity and ecosystem services can be achieved through conservation efforts, especially of keystone species. Giraffes (Giraffa camelopardalis) play an important role in sustaining Africa's ecosystems, but are 'vulnerable' according to the IUCN Red List since 2016. Monitoring an animal's behavior in the wild helps to develop and assess their conservation management. One mechanism for remote tracking of wildlife behavior is to attach accelerometers to animals to record their body movement. We tested two different commercially available high-resolution accelerometers, e-obs and Africa Wildlife Tracking (AWT), attached to the top of the heads of three captive giraffes and analyzed the accuracy of automatic behavior classifications, focused on the Random Forests algorithm. For both accelerometers, behaviors of lower variety in head and neck movements could be better predicted (i.e., feeding above eye level, mean prediction accuracy e-obs/AWT: 97.6%/99.7%; drinking: 96.7%/97.0%) than those with a higher variety of body postures (such as standing: 90.7-91.0%/75.2-76.7%; rumination: 89.6-91.6%/53.5-86.5%). Nonetheless both devices come with limitations and especially the AWT needs technological adaptations before applying it on animals in the wild. Nevertheless, looking at the prediction results, both are promising accelerometers for behavioral classification of giraffes. Therefore, these devices when applied to free-ranging animals, in combination with GPS tracking, can contribute greatly to the conservation of giraffes.
We report a comparison of two photonic techniques for single-molecule sensing: fluorescence nanoscopy and optoplasmonic sensing. As the test system, oligonucleotides with and without fluorescent labels are transiently hybridized to complementary "docking" strands attached to gold nanorods. Comparing the measured single-molecule kinetics helps to examine the influence of the fluorescent labels as well as factors arising from different sensing geometries. Our results demonstrate that DNA dissociation is not significantly altered by the fluorescent labels and that DNA association is affected by geometric factors in the two techniques. These findings open the door to exploiting plasmonic sensing and fluorescence nanoscopy in a complementary fashion, which will aid in building more powerful sensors and uncovering the intricate effects that influence the behavior of single molecules.
Due to their electrically polarized air-filled internal pores, optimized ferroelectrets exhibit a remarkable piezoelectric response, making them suitable for energy harvesting. Expanded polytetrafluoroethylene (ePTFE) ferroelectret films are laminated with two fluorinated-ethylene-propylene (FEP) copolymer films and internally polarized by corona discharge. Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS)-coated spandex fabric is employed for the electrodes to assemble an all-organic ferroelectret nanogenerator (FENG). The outer electret-plus-electrode double layers form active device layers with deformable electric dipoles that strongly contribute to the overall piezoelectric response in the proposed concept of wearable nanogenerators. Thus, the FENG with spandex electrodes generates a short-circuit current which is twice as high as that with aluminum electrodes. The stacking sequence spandex/FEP/ePTFE/FEP/ePTFE/FEP/spandex with an average pore size of 3 mu m in the ePTFE films yields the best overall performance, which is also demonstrated by the displacement-versus-electric-field loop results. The all-organic FENGs are stable up to 90 degrees C and still perform well 9 months after being polarized. An optimized FENG makes three light emitting diodes (LEDs) blink twice with the energy generated during a single footstep. The new all-organic FENG can thus continuously power wearable electronic devices and is easily integrated, for example, with clothing, other textiles, or shoe insoles.
In our fast-changing world, human-machine interfaces (HMIs) are of ever-increasing importance. Among the most ubiquitous examples are touchscreens that most people are familiar with from their smartphones. The quality of such an HMI can be improved by adding haptic feedback-an imitation of using mechanical buttons-to the touchscreen. Thin-film actuators on the basis of electro-mechanically active polymers (EAPs), with the electroactive material sandwiched between two compliant electrodes, offer a promising technology for haptic surfaces. In thin-film technology, the thickness and the number of stacked layers of the electroactive dielectric are key parameters for tuning a system. Therefore, we have experimentally investigated the influence of the thickness of a single EAP layer on the electrical and the electro-mechanical performance of the transducer. In order to achieve high electro-mechanical actuator outputs, we have employed relaxor-ferroelectric ter-fluoropolymers that can be screen-printed. By means of a model-based approach, we have also directly compared single- and multi-layer actuators, thus providing guidelines for optimized transducer configurations with respect to the system requirements of haptic applications for which the operation frequency is of particular importance.
The employment implications of decarbonizing the energy sector have received far less attention than the technology dimension of the transition, although being of critical importance to policymakers. In this work, we adapt a methodology based on employment factors to project future changes in quantity and composition of direct energy supply jobs for two scenarios - (1) relatively weak emissions reductions as pledged in the nationally determined contributions (NDC) and (2) stringent reductions compatible with the 1.5 °C target. We find that in the near-term the 1.5°C-compatible scenario results in a net increase in jobs through gains in solar and wind jobs in construction, installation, and manufacturing, despite significant losses in coal fuel supply; eventually leading to a peak in total direct energy jobs in 2025. In the long run, improvements in labour productivity lead to a decrease of total direct energy employment compared to today, however, total jobs are still higher in a 1.5 °C than in an NDC scenario. Operation and maintenance jobs dominate future jobs, replacing fuel supply jobs. The results point to the need for active policies aimed at retraining, both inside and outside the renewable energy sector, to complement climate policies within the concept of a “just transition”.
We examine the relationship between different types of power investments and regional economic dynamics. We construct a novel panel dataset combining data on regional GDP and power capacity additions for different technologies between 1960 and 2015, which covers 65% of the global power capacity that has been installed in this period. We use an event study design to identify the effect of power capacity addition on GDP per capita, exploiting the fact that the exact amount of power capacity coming online each year is determined by random construction delays. We find evidence that GDP per capita increases by 0.2% in the 6 years around the coming online of 100 MW coal-fired power capacity. We find similar effects for hydropower capacity, but not for any other type of power capacity. The positive effects are regionally bounded and stronger for projects on new sites (green-field). The magnitude of this effect might not be comparable to the total external costs of building new coal-fired power capacity, yet our results help to explain why policymakers favor coal investments for spurring regional growth.
In order to achieve their business goals, organizations heavily rely on the operational excellence of their business processes. In traditional scenarios, business processes are usually well-structured, clearly specifying when and how certain tasks have to be executed. Flexible and knowledge-intensive processes are gathering momentum, where a knowledge worker drives the execution of a process case and determines the exact process path at runtime. In the case of an exception, the knowledge worker decides on an appropriate handling. While there is initial work on exception handling in well-structured business processes, exceptions in case management have not been sufficiently researched. This paper proposes an exception handling framework for stage-oriented case management languages, namely Guard Stage Milestone Model, Case Management Model and Notation, and Fragment-based Case Management. The effectiveness of the framework is evaluated with two real-world use cases showing that it covers all relevant exceptions and proposed handling strategies.
Poly(vinylidenefluoride-trifluoroethylene)-based (P(VDF-TrFE)-based) terpolymers represent a new class of electroactive polymer materials that are relaxor-ferroelectric (RF) polymers and that offer unique and attractive property combinations in comparison with conventional ferroelectric polymers. The RF state is achieved by introducing a fluorine-containing termonomer as a "defect" into the ferroelectric P(VDF-TrFE) copolymer, which reduces the interaction between the VDF/TrFE dipoles. The resulting terpolymer exhibits a low Curie transition temperature and small remanent and coercive fields yielding a slim hysteresis loop that is typical for RF materials. Though the macroscopic behavior is similar to RF ceramics, the mechanisms of relaxor ferroelectricity in semi-crystalline polymers are different and not fully understood yet. Structure-property relationships play an important role in RF terpolymers, as they govern the final RF properties. Hence, a review of important characteristics, previous studies and relevant developments of P(VDF-TrFE)-based terfluoropolymers with either chlorofluoroethylene (CFE) or chlorotrifluoroethylene (CTFE) as the termonomer is deemed useful. The role of the termonomer and of its composition, as well as the effects of the processing conditions on the semi-crystalline structure which in turn affects the final RF properties are discussed in detail. In addition, the presence of noteworthy transition(s) in the mid-temperature range and the influence of preparation conditions on those transitions are reviewed. A better understanding of the fundamental aspects affecting the semi-crystalline structures will help to elucidate the nature of RF activity in VDF-based terpolymers and also help to further improve their applications-relevant electroactive properties.
Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target an ecosystem's weakest points. In Bitcoin, the exchange platforms are one of them. Each exchange breach is a threat not only to direct victims, but to the credibility of Bitcoin's entire ecosystem. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We show that while the security regimen of Bitcoin exchanges is subpar compared to other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. Furthermore we show that overall security posture has improved, but still has major flaws. To discover adversarial methods post-breach, we have analyzed two cases of BTC laundering. Through this analysis we provide insight into how exchange platforms with lax cyber security even further increase the intermediary risk introduced by them into the Bitcoin ecosystem.
The study explores differences between three user types in the top tweets about the 2015 “refugee crisis” in Germany and presents the results of a quantitative content analysis. All tweets with the keyword “Flüchtlinge” posted for a monthlong period following September 13, 2015, the day Germany decided to implement border controls, were collected (N = 763,752). The top 2,495 tweets according to number of retweets were selected for analysis. Differences between news media, public and private actor tweets in topics, tweet characteristics such as tone and opinion expression, links, and specific sentiments toward refugees were analyzed. We found strong differences between the tweets. Public actor tweets were the main source of positive sentiment toward refugees and the main information source on refugee support. News media tweets mostly reflected traditional journalistic norms of impartiality and objectivity, whereas private actor tweets were more diverse in sentiments toward refugees.
Today, firms pursuing a pioneering strategy are often engaged in supply chain relationships to benefit from external resources and to improve their innovation. However, this effort can be impeded by power asymmetries in such relationships and especially by the execution of coercive power by their partner firm. Contracts could potentially reduce this risk of opportunistic behavior. Our survey study on 778 small to medium-sized enterprises in the European packaging and medical equipment industries examines how coercive power of the partner and the contractual arrangement between firms moderate the pioneering strategy's innovation outcomes in the short and long run. Our results confirm the negative effect of coercive power on innovation performance in both the short and long term. However, the compensating effect of rather complete contracts differs temporally. Whereas, contract completeness protects against higher dependence at the beginning of the collaboration, their effect diminishes over time. In contrast, rather incomplete contracts enhance the innovation performance in the long term, possibly complemented with trust.
The usage of gamification in the contexts of commerce, consumption, innovation or eLearning in schools and universities has been extensively researched. However, the potentials of serious games to transfer and perpetuate knowledge and action patterns in learning factories have not been levered so far. The goal of this paper is to introduce a serious game as an instrument for knowledge transfer and perpetuation. Therefore, reqirements towards serious games in the context of learning factories are pointed out. As a result, that builds on these requirements, a serious learning game for the topic of Industry 4.0 is practically designed and evaluated.
Use of a standard non-rad-hard digital cell library in the rad-hard design can be a cost-effective solution for space applications. In this paper we demonstrate how a standard non-rad-hard flip-flop, as one of the most vulnerable digital cells, can be converted into a rad-hard flip-flop without modifying its internal structure. We present five variants of a Triple Modular Redundancy (TMR) flip-flop: baseline TMR flip-flop, latch-based TMR flip-flop, True-Single Phase Clock (TSPC) TMR flip-flop, scannable TMR flip-flop and self-correcting TMR flipflop. For all variants, the multi-bit upsets have been addressed by applying special placement constraints, while the Single Event Transient (SET) mitigation was achieved through the usage of customized SET filters and selection of optimal inverter sizes for the clock and reset trees. The proposed flip-flop variants feature differing performance, thus enabling to choose the optimal solution for every sensitive node in the circuit, according to the predefined design constraints. Several flip-flop designs have been validated on IHP's 130nm BiCMOS process, by irradiation of custom-designed shift registers. It has been shown that the proposed TMR flip-flops are robust to soft errors with a threshold Linear Energy Transfer (LET) from (32.4 MeV.cm(2)/mg) to (62.5 MeV.cm(2)/mg), depending on the variant.
Most machine learning methods provide only point estimates when being queried to predict on new data. This is problematic when the data is corrupted by noise, e.g. from imperfect measurements, or when the queried data point is very different to the data that the machine learning model has been trained with. Probabilistic modelling in machine learning naturally equips predictions with corresponding uncertainty estimates which allows a practitioner to incorporate information about measurement noise into the modelling process and to know when not to trust the predictions. A well-understood, flexible probabilistic framework is provided by Gaussian processes that are ideal as building blocks of probabilistic models. They lend themself naturally to the problem of regression, i.e., being given a set of inputs and corresponding observations and then predicting likely observations for new unseen inputs, and can also be adapted to many more machine learning tasks. However, exactly inferring the optimal parameters of such a Gaussian process model (in a computationally tractable manner) is only possible for regression tasks in small data regimes. Otherwise, approximate inference methods are needed, the most prominent of which is variational inference.
In this dissertation we study models that are composed of Gaussian processes embedded in other models in order to make those more flexible and/or probabilistic. The first example are deep Gaussian processes which can be thought of as a small network of Gaussian processes and which can be employed for flexible regression. The second model class that we study are Gaussian process state-space models. These can be used for time-series modelling, i.e., the task of being given a stream of data ordered by time and then predicting future observations. For both model classes the state-of-the-art approaches offer a trade-off between expressive models and computational properties (e.g. speed or convergence properties) and mostly employ variational inference. Our goal is to improve inference in both models by first getting a deep understanding of the existing methods and then, based on this, to design better inference methods. We achieve this by either exploring the existing trade-offs or by providing general improvements applicable to multiple methods.
We first provide an extensive background, introducing Gaussian processes and their sparse (approximate and efficient) variants. We continue with a description of the models under consideration in this thesis, deep Gaussian processes and Gaussian process state-space models, including detailed derivations and a theoretical comparison of existing methods.
Then we start analysing deep Gaussian processes more closely: Trading off the properties (good optimisation versus expressivity) of state-of-the-art methods in this field, we propose a new variational inference based approach. We then demonstrate experimentally that our new algorithm leads to better calibrated uncertainty estimates than existing methods.
Next, we turn our attention to Gaussian process state-space models, where we closely analyse the theoretical properties of existing methods.The understanding gained in this process leads us to propose a new inference scheme for general Gaussian process state-space models that incorporates effects on multiple time scales. This method is more efficient than previous approaches for long timeseries and outperforms its comparison partners on data sets in which effects on multiple time scales (fast and slowly varying dynamics) are present.
Finally, we propose a new inference approach for Gaussian process state-space models that trades off the properties of state-of-the-art methods in this field. By combining variational inference with another approximate inference method, the Laplace approximation, we design an efficient algorithm that outperforms its comparison partners since it achieves better calibrated uncertainties.
With the advent of big data and data lakes, data are often integrated from multiple sources. Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth. One way to check the quality of data is to infer functional dependencies (fds). However, in many modern applications it might be necessary to extract properties and relationships that are not captured through fds, due to the necessity to admit exceptions, or to consider similarity rather than equality of data values. Relaxed fds (rfds) have been introduced to meet these needs, but their discovery from data adds further complexity to an already complex problem, also due to the necessity of specifying similarity and validity thresholds. We propose Domino, a new discovery algorithm for rfds that exploits the concept of dominance in order to derive similarity thresholds of attribute values while inferring rfds. An experimental evaluation on real datasets demonstrates the discovery performance and the effectiveness of the proposed algorithm.
Germany's electronic patient record ("ePA") launched in 2021 with several attempts and years of delay. The development of such a large-scale project is a complex task, and so is its adoption. Individual attitudes towards an electronic health record are crucial, as individuals can reject opting-in to it and making any national efforts unachievable. Although the integration of an electronic health record serves potential benefits, it also constitutes risks for an individual's privacy. With a mixed-methods study design, this work provides evidence that different types of motivations and contextual privacy antecedents affect usage intentions towards the ePA. Most significantly, individual motivations stemming from feelings of volition or external mandates positively affect ePA adoption, although internal incentives are more powerful.
Non-geminate recombination, as one of the most relevant loss mechanisms in organic and perovskite solar cells, deserves special attention in research efforts to further increase device performance. It can be subdivided into first, second, and third order processes, which can be elucidated by the effects that they have on the time-dependent open-circuit voltage decay. In this study, analytical expressions for the open-circuit voltage decay exhibiting one of the aforementioned recombination mechanisms were derived. It was possible to support the analytical models with experimental examples of three different solar cells, each of them dominated either by first (PBDBT:CETIC-4F), second (PM6:Y6), or third (irradiated CH3NH3PbI3) order recombination. Furthermore, a simple approach to estimate the dominant recombination process was also introduced and tested on these examples. Moreover, limitations of the analytical models and the measurement technique itself were discussed.
This study investigated how touching and being touched by a humanoid robot affects human physiology, impressions of the interaction, and attitudes towards humanoid robots. 21 healthy adult participants completed a 3 (touch style: touching, being touched, pointing) x 2 (body part: hand vs buttock) within-subject design using a Pepper robot. Skin conductance response (SCR) was measured during each interaction. Perceived impressions of the interaction (i.e., friendliness, comfort, arousal) were measured per questionnaire after each interaction. Participants' demographics and their attitude towards robots were also considered. We found shorter SCR rise times in the being touched compared to the touching condition, possibly reflecting psychological alertness to the unpredictability of robot-initiated contacts. The hand condition had shorter rise times than the buttock condition. Most participants evaluated the hand condition as most friendly and comfortable and the robot-initiated interactions as most arousing. Interacting with Pepper improved attitudes towards robots. Our findings require future studies with larger samples and improved procedures. They have implications for robot design in all domains involving tactile interactions, such as caring and intimacy.
Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils' SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R-2) = 0.91; root mean square error (RMSE) = 0.11% and R-2 = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R-2 = 0.88, RMSE = 0.07%; R-2 = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.
CxNy
(2021)
The search for metal-free and visible light-responsive materials for photocatalytic applications has attracted the interest of not only academics but also the industry in the last decades. Since graphitic carbon nitride (g-C3N4) was first reported as a metal-free photocatalyst, this has been widely investigated in different light-driven reactions. However, the high recombination rate, low electrical conductivity, and lack of photoresponse in most of the visible range have elicited the search for alternatives. In this regard, a broad family of carbon nitride (CxNy) materials was anticipated several decades ago. However, the attention of the researchers in these materials has just been awakened in the last years due to the recent success in the syntheses of some of these materials (i.e., C3N3, C2N, C3N, and C3N5, among others), together with theoretical simulations pointing at the excellent physico-chemical properties (i.e., crystalline structure and chemical morphology, electronic configuration and semiconducting nature, or high refractive index and hardness, among others) and optoelectronic applications of these materials. The performance of CxNy, beyond C3N4, has been barely evaluated in real applications, including energy conversion, storage, and adsorption technologies, and further work must be carried out, especially experimentally, in order to confirm the high expectations raised by simulations and theoretical calculations. Herein, we have summarized the scarce literature related to recent results reporting the synthetic routes, structures, and performance of these materials as photocatalysts. Moreover, the challenges and perspectives at the forefront of this field using CxNy materials are disclosed. We aim to stimulate the research of this new generation of CxNy-based photocatalysts, beyond C3N4, with improved photocatalytic efficiencies by harnessing the striking structural, electronic, and optical properties of this new family of materials.
Polymeric devices capable of releasing submicron particles (subMP) on demand are highly desirable for controlled release systems, sensors, and smart surfaces. Here, a temperature-memory polymer sheet with a programmable smooth surface served as matrix to embed and release polystyrene subMP controlled by particle size and temperature. subMPs embedding at 80 degrees C can be released sequentially according to their size (diameter D of 200 nm, 500 nm, 1 mu m) when heated. The differences in their embedding extent are determined by the various subMPs sizes and result in their distinct release temperatures. Microparticles of the same size (D approximate to 1 mu m) incorporated in films at different programming temperatures T-p (50, 65, and 80 degrees C) lead to a sequential release based on the temperature-memory effect. The change of apparent height over the film surface is quantified using atomic force microscopy and the realization of sequential release is proven by confocal laser scanning microscopy. The demonstration and quantification of on demand subMP release are of technological impact for assembly, particle sorting, and release technologies in microtechnology, catalysis, and controlled release.
The retention of actinides in different oxidation states (An(X), X = III, IV, VI) by a calcium-silicate-hydrate (C-S-H) phase with a Ca/Si (C/S) ratio of 0.8 was investigated in the presence of gluconate (GLU). The actinides considered were Am(III), Th(IV), Pu(IV), and U(VI). Eu(III) was investigated as chemical analogue for Am(III) and Cm(III). In addition to the ternary systems An(X)/GLU/C-S-H, also binary systems An(X)/C-S-H, GLU/C-S-H, and An(X)/GLU were studied. Complementary analytical techniques were applied to address the different specific aspects of the binary and ternary systems. Time-resolved laser-induced luminescence spectroscopy (TRLFS) was applied in combination with parallel factor analysis (PARAFAC) to identify retained species and to monitor species-selective sorption kinetics. ¹³C and ²⁹Si magic-angle-spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy and X-ray photoelectron spectroscopy (XPS) were applied to determine the bulk structure and the composition of the C-S-H surface, respectively, in the absence and presence of GLU. The interaction of Th(IV) with GLU in different electrolytes was studied by capillary electrophoresis-inductively coupled plasma mass spectrometry (CE-ICP-MS). The influence of GLU on An(X) retention was investigated for a large concentration range up to 10⁻² M. The results showed that GLU had little to no effect on the overall An(X) retention by C-S-H with C/S of 0.8, regardless of the oxidation state of the actinides. For Eu(III), the TRLFS investigations additionally implied the formation of a Eu(III)-bearing precipitate with dissolved constituents of the C-S-H phase, which becomes structurally altered by the presence of GLU. For U(VI) sorption on the C-S-H phase, only a small influence of GLU could be established in the luminescence spectroscopic investigations, and no precipitation of U(VI)-containing secondary phases could be identified.
The economic context for renewable power in Europe is shifting: feed-in tariffs are replaced by auctioned premiums as the main support schemes. As renewables approach competitiveness, political pressure mounts to phase out support, whereas some other actors perceive a need for continued fixed-price support. We investigate how the phase-out of support or the reintroduction of feed-in tariffs would affect investors' choices for renewables through a conjoint analysis. In particular, we analyse the impact of coordination - the simultaneousness - of policy changes across countries and technologies. We find that investment choices are not strongly affected if policy changes are coordinated and returns unaffected. However, if policy changes are uncoordinated, investments shift to still supported - less mature and costlier - technologies or countries where support remains or is reintroduced. This shift is particularly strong for large investors and could potentially skew the European power mix towards an over-reliance on a single, less mature technology or specific generation region, resulting in a more expensive power system. If European countries want to change their renewable power support policies, and especially if they phase out support and expose renewables to market competition, it is important that they coordinate their actions.
We present a systematic study on the properties of Na(Y,Gd)F-4-based upconverting nanoparticles (UCNP) doped with 18% Yb3+, 2% Tm3+, and the influence of Gd3+ (10-50 mol% Gd3+). UCNP were synthesized via the solvothermal method and had a range of diameters within 13 and 50 nm. Structural and photophysical changes were monitored for the UCNP samples after a 24-month incubation period in dry phase and further redispersion. Structural characterization was performed by means of X-ray diffraction (XRD), transmission electron microscopy (TEM) as well as dynamic light scattering (DLS), and the upconversion luminescence (UCL) studies were executed at various temperatures (from 4 to 295 K) using time-resolved and steady-state spectroscopy. An increase in the hexagonal lattice phase with the increase of Gd3+ content was found, although the cubic phase was prevalent in most samples. The Tm3+-luminescence intensity as well as the Tm3+-luminescence decay times peaked at the Gd3+ concentration of 30 mol%. Although the general upconverting luminescence properties of the nanoparticles were preserved, the 24-month incubation period lead to irreversible agglomeration of the UCNP and changes in luminescence band ratios and lifetimes.
The shape and the actuation capability of state of the art robotic devices typically relies on multimaterial systems from a combination of geometry determining materials and actuation components. Here, we present multifunctional 4D-actuators processable by 3D-printing, in which the actuator functionality is integrated into the shaped body. The materials are based on crosslinked poly(carbonate-urea-urethane) networks (PCUU), synthesized in an integrated process, applying reactive extrusion and subsequent water-based curing. Actuation capability could be added to the PCUU, prepared from aliphatic oligocarbonate diol, isophorone diisocyanate (IPDI) and water, in a thermomechanical programming process. When programmed with a strain of epsilon(prog) = 1400% the PCUU networks exhibited actuation apparent by reversible elongation epsilon'(rev) of up to 22%. In a gripper a reversible bending epsilon'(rev)((be)(nd)()) in the range of 37-60% was achieved when the actuation temperature (T-high) was varied between 45 degrees C and 49 degrees C. The integration of actuation and shape formation could be impressively demonstrated in two PCUU-based reversible fastening systems, which were able to hold weights of up to 1.1 kg. In this way, the multifunctional materials are interesting candidate materials for robotic applications where a freedom in shape design and actuation is required as well as for sustainable fastening systems.
In this paper, an asynchronous design for soft error detection and correction in combinational and sequential circuits is presented. The proposed architecture is called Asynchronous Full Error Detection and Correction (AFEDC). A custom design flow with integrated commercial EDA tools generates the AFEDC using the asynchronous bundled-data design style. The AFEDC relies on an Error Detection Circuit (EDC) for protecting the combinational logic and fault-tolerant latches for protecting the sequential logic. The EDC can be implemented using different detection methods. For this work, two boundary variants are considered, the Full Duplication with Comparison (FDC) and the Partial Duplication with Parity Prediction (PDPP). The AFEDC architecture can handle single events and timing faults of arbitrarily long duration as well as the synchronous FEDC, but additionally can address known metastability issues of the FEDC and other similar synchronous architectures and provide a more practical solution for handling the error recovery process. Two case studies are developed, a carry look-ahead adder and a pipelined non-restoring array divider. Results show that the AFEDC provides equivalent fault coverage when compared to the FEDC while reducing area, ranging from 9.6% to 17.6%, and increasing energy efficiency, which can be up to 6.5%.
A new variant of the Laser-Induced Pressure-Pulse (LIPP) method for repeatable, time-resolved space-charge profile measurements is proposed and demonstrated. Automated deposition of a fresh laser-target film before each illumination leads to good repeatability of the LIPP and thus allows for the detection of time-resolved changes in the space-charge distribution over many hours. We describe and discuss the experimental setup and its features, compare the repeatability of the LIPP measurements on the same sample without and with re-preparation of the test cell, and present the time-resolved evolution of the space-charge profile in a two-layer arrangement of a silicone-grease and a silicone-elastomer film as an example. Finally, the temperature dependence of the space-charge evolution during polarization under high voltage and during depolarization in short circuit is shown. Possible uses and future developments of the new LIPP approach are also discussed.
We search for homovalent alternatives for A, B, and X-ions in ABX(3) type inorganic halide perovskites suitable for tandem solar cell applications. We replace the conventional A-site organic cation CH3NH3, by 3 inorganic cations, Cs, K, and Rb, and the B site consists of metals; Cd, Hg, Ge, Pb, and Sn This work is built on our previous high throughput screening of hybrid perovskite materials (Kar et al 2018 J. Chem. Phys. 149, 214701). By performing a systematic screening study using Density Functional Theory (DFT) methods, we found 11 suitable candidates; 2 Cs-based, 3 K-based and 6 Rb-based that are suitable for tandem solar cell applications.
A method for the fabrication of well-defined metallic nanostructures is presented here in a simple and straightforward fashion. As an alternative to lithographic techniques, this routine employs microcontact printing utilizing wrinkled stamps, which are prepared from polydimethylsiloxane (PDMS), and includes the formation of hydrophobic stripe patterns on a substrate via the transfer of oligomeric PDMS. Subsequent backfilling of the interspaces between these stripes with a hydroxyl-functional poly(2-vinyl pyridine) then provides the basic pattern for the deposition of citrate-stabilized gold nanoparticles promoted by electrostatic interaction. The resulting metallic nanostripes can be further customized by peeling off particles in a second microcontact printing step, which employs poly(ethylene imine) surface-decorated wrinkled stamps, to form nanolattices. Due to the independent adjustability of the period dimensions of the wrinkled stamps and stamp orientation with respect to the substrate, particle arrays on the (sub)micro-scale with various kinds of geometries are accessible in a straightforward fashion. This work provides an alternative, cost-effective, and scalable surface-patterning technique to fabricate nanolattice structures applicable to multiple types of functional nanoparticles. Being a top-down method, this process could be readily implemented into, e.g., the fabrication of optical and sensing devices on a large scale.
There is an increasing interest in fusing data from heterogeneous sources. Combining data sources increases the utility of existing datasets, generating new information and creating services of higher quality. A central issue in working with heterogeneous sources is data migration: In order to share and process data in different engines, resource intensive and complex movements and transformations between computing engines, services, and stores are necessary.
Muses is a distributed, high-performance data migration engine that is able to interconnect distributed data stores by forwarding, transforming, repartitioning, or broadcasting data among distributed engines' instances in a resource-, cost-, and performance-adaptive manner. As such, it performs seamless information sharing across all participating resources in a standard, modular manner. We show an overall improvement of 30 % for pipelining jobs across multiple engines, even when we count the overhead of Muses in the execution time. This performance gain implies that Muses can be used to optimise large pipelines that leverage multiple engines.
Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients' anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.
Cold plasma is considered to be a novel, non-thermal, chemical-free and eco-friendly disinfection and surface modification technology. Plasma treatment of air to generate the so called plasma processed air (PPA) induces the formation of reactive oxygen (ROS) and nitrogen species (RNS). As a result, PPA has a different chemical composition compared to untreated air and suits therefore as an alternative method for microbial disinfection. However, depending on the product properties of the food matrix and its composition, a number of plasmainduced reactions also need to be taken into consideration.
This necessitates also the elucidation and understanding of the basic interactions of plasma species with bioactive compounds. The intention here is to avoid the degradation of these valuable substances and to prevent other undesirable effects in future food related applications.
In the present study, the effects of PPA treatment on selected antioxidants such as pyrocatechol and derivatives of hydroxycinnimic acid were investigated in model systems to specify possible reactions induced. Antioxidant capacity, pH value, UV-Vis spectroscopy, RP-HPLC and LC-MS analysis were applied to identify reaction products providing information on possible changes induced in food matrices by PPA treatment.
Exposure to PPA caused a perceptible color change towards yellow-brown accompanied by a strong reduction of the pH and the formation of insoluble sediments in the model solutions. The accumulation of nitrate, nitrite, but not of hydrogen peroxide was shown. LC-MS analysis demonstrated the formation of plasma-modified derivatives in all tested systems. The main reactions in liquid model solutions exposed to PPA were attributed to oxidation, nitration and polymerization of the phenolic compounds.
An apple a day
(2021)
The healthcare industry has been slow to adopt new technologies and practices. However, digital and data-enabled innovations diffuse the market, and the COVID-19 pandemic has recently emphasized the necessity of a fundamental digital transformation. Available research indicates the relevance of digital platforms in this process but has not studied their economic impact to date. In view of this research gap and the social and economic relevance of healthcare, we explore how digital platforms might affect value creation in this market with a particular focus on Google, Apple, Facebook, Amazon, and Microsoft (GAFAM). We rely on value network analyses to examine how GAFAM platforms introduce new value-creating roles and mechanisms in healthcare through their manifold products and services. Hereupon, we examine the GAFAM-impact on healthcare by scrutinizing the facilitators, activities, and effects. Our analyses show how GAFAM platforms multifacetedly untie conventional relationships and transform value creation structures in the healthcare market.
Nanoporous carbon materials (NCMs) provide the "function" of high specific surface area and thus have large interface area for interactions with surrounding species, which is of particular importance in applications related to adsorption processes. The strength and mechanism of adsorption depend on the pore architecture of the NCMs. In addition, chemical functionalization can be used to induce changes of electron density and/or electron density distribution in the pore walls, thus further modifying the interactions between carbons and guest species. Typical approaches for functionalization of nanoporous materials with regular atomic construction like porous silica, metal-organic frameworks, or zeolites, cannot be applied to NCMs due to their less defined local atomic construction and abundant defects. Therefore, synthetic strategies that offer a higher degree of control over the process of functionalization are needed. Synthetic approaches for covalent functionalization of NCMs, that is, for the incorporation of heteroatoms into the carbon backbone, are critically reviewed with a special focus on strategies following the concept "from molecules to materials." Approaches for coordinative functionalization with metallic species, and the functionalization by nanocomposite formation between pristine carbon materials and heteroatom-containing carbons, are introduced as well. Particular focus is given to the influences of these functionalizations in adsorption-related applications.
Transitioning German road transport partially to hydrogen energy is among the possibilities being discussed to help meet national climate targets. This study investigates impacts of a hypothetical, complete transition from conventionally-fueled to hydrogen-powered German transport through representative scenarios. Our results show that German emissions change between -179 and +95 MtCO(2)eq annually, depending on the scenario, with renewable-powered electrolysis leading to the greatest emissions reduction, while electrolysis using the fossilintense current electricity mix leads to the greatest increase. German energy emissions of regulated pollutants decrease significantly, indicating the potential for simultaneous air quality improvements. Vehicular hydrogen demand is 1000 PJ annually, requiring 446-525 TWh for electrolysis, hydrogen transport and storage, which could be supplied by future German renewable generation, supporting the potential for CO2-free hydrogen traffic and increased energy security. Thus hydrogen-powered transport could contribute significantly to climate and air quality goals, warranting further research and political discussion about this possibility.
The prehistory of electrets is not known yet, but it is quite likely that the electrostatic charging behavior of amber (Greek: τò ηλεκτρoν, i.e., “electron”) already was familiar to people in ancient cultures (China, Egypt, Greece, etc.), before the Greek philosopher and scientist Thales of Miletus (6th century BCE)-or rather his disciples and followers-reported it in writing (cf. Figure 1). More than two millennia later, William Gilbert (1544–1603), the physician of Queen Elizabeth I, coined the term “electric” in his book De Magnete, Magneticisque Corporibus, et de Magno Magnete Tellure (1600) for dielectric materials that attract like amber and that included sulfur and glass [1]. The second half of the 18th century saw the invention of the electrophorus or electrophore [2], a capacitive electret device, in 1762 by Johan Carl Wilcke (1732–1796).
Argument mining on twitter
(2021)
In the last decade, the field of argument mining has grown notably. However, only relatively few studies have investigated argumentation in social media and specifically on Twitter. Here, we provide the, to our knowledge, first critical in-depth survey of the state of the art in tweet-based argument mining. We discuss approaches to modelling the structure of arguments in the context of tweet corpus annotation, and we review current progress in the task of detecting argument components and their relations in tweets. We also survey the intersection of argument mining and stance detection, before we conclude with an outlook.
Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.940.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}�∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.940.94 for ε=6�=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.760.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.
Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.940.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}�∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.940.94 for ε=6�=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.760.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.
The photoinduced nonadiabatic dynamics of the enol-keto isomerization of 10-hydroxybenzo[h]quinoline (HBQ) are studied computationally using high-dimensional quantum dynamics. The simulations are based on a diabatic vibronic coupling Hamiltonian, which includes the two lowest pi pi* excited states and a n pi* state, which has high energy in the Franck-Condon zone, but significantly stabilizes upon excited state intramolecular proton transfer. A procedure, applicable to large classes of excited state proton transfer reactions, is presented to parametrize this model using potential energies, forces and force constants, which, in this case, are obtained by time-dependent density functional theory. The wave packet calculations predict a time scale of 10-15 fs for the photoreaction, and reproduce the time constants and the coherent oscillations observed in time- resolved spectroscopic studies performed on HBQ. In contrast to the interpretation given to the most recent experiments, it is found that the reaction initiated by 1 pi pi* <- S-0 photoexcitation proceeds essentially on a single potential energy surface, and the observed coherences bear signatures of Duschinsky mode-mixing along the reaction path. The dynamics after the 2 pi pi* <- S-0 excitation are instead nonadiabatic, and the n pi* state plays a major role in the relaxation process. The simulations suggest a mainly active role of the proton in the isomerization, rather than a passive migration assisted by the vibrations of the benzoquinoline backbone. <br /> [GRAPHICS] <br /> .
A comprehensive workflow to analyze ensembles of globally inverted 2D electrical resistivity models
(2022)
Electrical resistivity tomography (ERT) aims at imaging the subsurface resistivity distribution and provides valuable information for different geological, engineering, and hydrological applications. To obtain a subsurface resistivity model from measured apparent resistivities, stochastic or deterministic inversion procedures may be employed. Typically, the inversion of ERT data results in non-unique solutions; i.e., an ensemble of different models explains the measured data equally well. In this study, we perform inference analysis of model ensembles generated using a well-established global inversion approach to assess uncertainties related to the nonuniqueness of the inverse problem. Our interpretation strategy starts by establishing model selection criteria based on different statistical descriptors calculated from the data residuals. Then, we perform cluster analysis considering the inverted resistivity models and the corresponding data residuals. Finally, we evaluate model uncertainties and residual distributions for each cluster. To illustrate the potential of our approach, we use a particle swarm optimization (PSO) algorithm to obtain an ensemble of 2D layer-based resistivity models from a synthetic data example and a field data set collected in Loon-Plage, France. Our strategy performs well for both synthetic and field data and allows us to extract different plausible model scenarios with their associated uncertainties and data residual distributions. Although we demonstrate our workflow using 2D ERT data and a PSObased inversion approach, the proposed strategy is general and can be adapted to analyze model ensembles generated from other kinds of geophysical data and using different global inversion approaches.
SensorHub
(2022)
Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects' real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub's technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.
Electrochemical biosensors employing natural and artificial heme peroxidases on semiconductors
(2020)
Heme peroxidases are widely used as biological recognition elements in electrochemical biosensors for hydrogen peroxide and phenolic compounds. Various nature-derived and fully synthetic heme peroxidase mimics have been designed and their potential for replacing the natural enzymes in biosensors has been investigated. The use of semiconducting materials as transducers can thereby offer new opportunities with respect to catalyst immobilization, reaction stimulation, or read-out. This review focuses on approaches for the construction of electrochemical biosensors employing natural heme peroxidases as well as various mimics immobilized on semiconducting electrode surfaces. It will outline important advances made so far as well as the novel applications resulting thereof.
The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.
The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.
Electrochemical methods offer the simple characterization of the synthesis of molecularly imprinted polymers (MIPs) and the readouts of target binding. The binding of electroinactive analytes can be detected indirectly by their modulating effect on the diffusional permeability of a redox marker through thin MIP films. However, this process generates an overall signal, which may include nonspecific interactions with the nonimprinted surface and adsorption at the electrode surface in addition to (specific) binding to the cavities. Redox-active low-molecular-weight targets and metalloproteins enable a more specific direct quantification of their binding to MIPs by measuring the faradaic current. The in situ characterization of enzymes, MIP-based mimics of redox enzymes or enzyme-labeled targets, is based on the indication of an electroactive product. This approach allows the determination of both the activity of the bio(mimetic) catalyst and of the substrate concentration.
Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep rein- forcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensor- and process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
The digitalization of value networks holds out the prospect of many advantages for the participating compa- nies. Utilizing information platforms, cross-company data exchange enables increased efficiency of collab- oration and offers space for new business models and services. In addition to the technological challenges, the fear of know-how leakage appears to be a significant roadblock that hinders the beneficial realization of new business models in digital ecosystems. This paper provides the necessary building blocks of digital participation and, in particular, classifies the issue of trust creation within it as a significant success factor. Based on these findings, it presents a solution concept that, by linking the identified building blocks, offers the individual actors of the digital value network the opportunity to retain sovereignty over their data and know-how and to use the potential of extensive networking. In particular, the presented concept takes into account the relevant dilemma, that every actor (e. g. the machine users) has to be able to control his commu- nicated data at any time and have sufficient possibilities for intervention that, on the one hand, satisfy the need for protection of his knowledge and, on the other hand, do not excessively diminish the benefits of the system or the business. Taking up this perspective, this paper introduces dedicated data sovereignty and shows a possible implementation concept.
Powered by blockchain
(2020)
Purpose: The purpose of this study is to formulate the most probable future scenario for the use of blockchain technology within the next 5–10 years in the electricity sector based on today’s experts’ views.
Design/methodology/approach: An international, two-stage Delphi study with 20 projections is used.
Findings: According to the experts, blockchain applications will be primarily based on permissioned or consortium blockchains. Blockchain-based applications will integrate Internet of Things devices in the power grid, manage the e-mobility infrastructure, automate billing and direct payment and issue certificates regarding the origin of electricity. Blockchain solutions are expected to play an important big role in fostering peer-to-peer trading in microgrids, further democratizing and decentralizing the energy sector. New regulatory frameworks become necessary.
Research limitations/implications: The Delphi study’s scope is rather broad than narrow and detailed. Further studies should focus on partial scenarios.
Practical implications: Electricity market participants should build blockchain-based competences and collaborate in current pilot projects.
Social implications: Blockchain technology will further decentralize the energy sector and probably reduce transaction costs. Originality/value: Despite the assumed importance of blockchain technology, no coherent foresight study on its use and implications exists yet. This study closes this research gap.
Recreational exercising and self-reported cardiometabolic diseases in German people living with HIV
(2021)
Exercise is known for its beneficial effects on preventing cardiometabolic diseases (CMDs) in the general population. People living with the human immunodeficiency virus (PLWH) are prone to sedentarism, thus raising their already elevated risk of developing CMDs in comparison to individuals without HIV. The aim of this cross-sectional study was to determine if exercise is associated with reduced risk of self-reported CMDs in a German HIV-positive sample (n = 446). Participants completed a self-report survey to assess exercise levels, date of HIV diagnosis, CD4 cell count, antiretroviral therapy, and CMDs. Participants were classified into exercising or sedentary conditions. Generalized linear models with Poisson regression were conducted to assess the prevalence ratio (PR) of PLWH reporting a CMD. Exercising PLWH were less likely to report a heart arrhythmia for every increase in exercise duration (PR: 0.20: 95% CI: 0.10–0.62, p < 0.01) and diabetes mellitus for every increase in exercise session per week (PR: 0.40: 95% CI: 0.10–1, p < 0.01). Exercise frequency and duration are associated with a decreased risk of reporting arrhythmia and diabetes mellitus in PLWH. Further studies are needed to elucidate the mechanisms underlying exercise as a protective factor for CMDs in PLWH.
The synthesis, structure, and photocatalytic water splitting performance of two new titania (TiO2)/gold(Au)/Bombyx mori silk hybrid materials are reported. All materials are monoliths with diameters of up to ca. 4.5 cm. The materials are macroscopically homogeneous and porous with surface areas between 170 and 210 m(2)/g. The diameter of the TiO2 nanoparticles (NPs) - mainly anatase with a minor fraction of brookite - and the Au NPs are on the order of 5 and 7-18 nm, respectively. Addition of poly(ethylene oxide) to the reaction mixture enables pore size tuning, thus providing access to different materials with different photocatalytic activities. Water splitting experiments using a sunlight simulator and a Xe lamp show that the new hybrid materials are effective water splitting catalysts and produce up to 30 mmol of hydrogen per 24 h. Overall the article demonstrates that the combination of a renewable and robust scaffold such as B. mori silk with a photoactive material provides a promising approach to new monolithic photocatalysts that can easily be recycled and show great potential for application in lightweight devices for green fuel production.
The simultaneous detection of energy, momentum and temporal information in electron spectroscopy is the key aspect to enhance the detection efficiency in order to broaden the range of scientific applications. Employing a novel 60 degrees wide angle acceptance lens system, based on an additional accelerating electron optical element, leads to a significant enhancement in transmission over the previously employed 30 degrees electron lenses. Due to the performance gain, optimized capabilities for time resolved electron spectroscopy and other high transmission applications with pulsed ionizing radiation have been obtained. The energy resolution and transmission have been determined experimentally utilizing BESSY II as a photon source. Four different and complementary lens modes have been characterized. (C) 2017 The Authors. Published by Elsevier B.V.
TerraSAR-X time series fill a gap in spaceborne snowmelt monitoring of small arctic catchments
(2018)
The timing of snowmelt is an important turning point in the seasonal cycle of small Arctic catchments. The TerraSAR-X (TSX) satellite mission is a synthetic aperture radar system (SAR) with high potential to measure the high spatiotemporal variability of snow cover extent (SCE) and fractional snow cover (FSC) on the small catchment scale. We investigate the performance of multi-polarized and multi-pass TSX X-Band SAR data in monitoring SCE and FSC in small Arctic tundra catchments of Qikiqtaruk (Herschel Island) off the Yukon Coast in the Western Canadian Arctic. We applied a threshold based segmentation on ratio images between TSX images with wet snow and a dry snow reference, and tested the performance of two different thresholds. We quantitatively compared TSX- and Landsat 8-derived SCE maps using confusion matrices and analyzed the spatiotemporal dynamics of snowmelt from 2015 to 2017 using TSX, Landsat 8 and in situ time lapse data. Our data showed that the quality of SCE maps from TSX X-Band data is strongly influenced by polarization and to a lesser degree by incidence angle. VH polarized TSX data performed best in deriving SCE when compared to Landsat 8. TSX derived SCE maps from VH polarization detected late lying snow patches that were not detected by Landsat 8. Results of a local assessment of TSX FSC against the in situ data showed that TSX FSC accurately captured the temporal dynamics of different snow melt regimes that were related to topographic characteristics of the studied catchments. Both in situ and TSX FSC showed a longer snowmelt period in a catchment with higher contributions of steep valleys and a shorter snowmelt period in a catchment with higher contributions of upland terrain. Landsat 8 had fundamental data gaps during the snowmelt period in all 3 years due to cloud cover. The results also revealed that by choosing a positive threshold of 1 dB, detection of ice layers due to diurnal temperature variations resulted in a more accurate estimation of snow cover than a negative threshold that detects wet snow alone. We find that TSX X-Band data in VH polarization performs at a comparable quality to Landsat 8 in deriving SCE maps when a positive threshold is used. We conclude that TSX data polarization can be used to accurately monitor snowmelt events at high temporal and spatial resolution, overcoming limitations of Landsat 8, which due to cloud related data gaps generally only indicated the onset and end of snowmelt.
Yedoma-extremely ice-rich permafrost with massive ice wedges formed during the Late Pleistocene-is vulnerable to thawing and degradation under climate warming. Thawing of ice-rich Yedoma results in lowering of surface elevations. Quantitative knowledge about surface elevation changes helps us to understand the freeze-thaw processes of the active layer and the potential degradation of Yedoma deposits. In this study, we use C-band Sentinel-1 InSAR measurements to map the elevation changes over ice-rich Yedoma uplands on Sobo-Sise Island, Lena Delta with frequent revisit observations (as short as six or 12 days). We observe significant seasonal thaw subsidence during summer months and heterogeneous inter-annual elevation changes from 2016-17. We also observe interesting patterns of stronger seasonal thaw subsidence on elevated flat Yedoma uplands by comparing to the surrounding Yedoma slopes. Inter-annual analyses from 2016-17 suggest that our observed positive surface elevation changes are likely caused by the delayed progression of the thaw season in 2017, associated with mean annual air temperature fluctuations.
Nanoporous carbon materials (NCMs) provide the "function" of high specific surface area and thus have large interface area for interactions with surrounding species, which is of particular importance in applications related to adsorption processes. The strength and mechanism of adsorption depend on the pore architecture of the NCMs. In addition, chemical functionalization can be used to induce changes of electron density and/or electron density distribution in the pore walls, thus further modifying the interactions between carbons and guest species. Typical approaches for functionalization of nanoporous materials with regular atomic construction like porous silica, metal-organic frameworks, or zeolites, cannot be applied to NCMs due to their less defined local atomic construction and abundant defects. Therefore, synthetic strategies that offer a higher degree of control over the process of functionalization are needed. Synthetic approaches for covalent functionalization of NCMs, that is, for the incorporation of heteroatoms into the carbon backbone, are critically reviewed with a special focus on strategies following the concept "from molecules to materials." Approaches for coordinative functionalization with metallic species, and the functionalization by nanocomposite formation between pristine carbon materials and heteroatom-containing carbons, are introduced as well. Particular focus is given to the influences of these functionalizations in adsorption-related applications.
TRIPOD
(2021)
Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.
Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.
Rotational motions play a key role in measuring seismic wavefield properties. Using newly developed portable rotational instruments, it is now possible to directly measure rotational motions in a broad frequency range. Here, we investigated the instrumental self-noise and data quality in a huddle test in Fürstenfeldbruck, Germany, in August 2019. We compare the data from six rotational and three translational sensors. We studied the recorded signals using correlation, coherence analysis, and probabilistic power spectral densities. We sorted the coherent noise into five groups with respect to the similarities in frequency content and shape of the signals. These coherent noises were most likely caused by electrical devices, the dehumidifier system in the building, humans, and natural sources such as wind. We calculated self-noise levels through probabilistic power spectral densities and by applying the Sleeman method, a three-sensor method. Our results from both methods indicate that self-noise levels are stable between 0.5 and 40 Hz. Furthermore, we recorded the 29 August 2019 ML 3.4 Dettingen earthquake. The calculated source directions are found to be realistic for all sensors in comparison to the real back azimuth. We conclude that the five tested blueSeis-3A rotational sensors, when compared with respect to coherent noise, self-noise, and source direction, provide reliable and consistent results. Hence, field experiments with single rotational sensors can be undertaken.
Rotational motions play a key role in measuring seismic wavefield properties. Using newly developed portable rotational instruments, it is now possible to directly measure rotational motions in a broad frequency range. Here, we investigated the instrumental self-noise and data quality in a huddle test in Fürstenfeldbruck, Germany, in August 2019. We compare the data from six rotational and three translational sensors. We studied the recorded signals using correlation, coherence analysis, and probabilistic power spectral densities. We sorted the coherent noise into five groups with respect to the similarities in frequency content and shape of the signals. These coherent noises were most likely caused by electrical devices, the dehumidifier system in the building, humans, and natural sources such as wind. We calculated self-noise levels through probabilistic power spectral densities and by applying the Sleeman method, a three-sensor method. Our results from both methods indicate that self-noise levels are stable between 0.5 and 40 Hz. Furthermore, we recorded the 29 August 2019 ML 3.4 Dettingen earthquake. The calculated source directions are found to be realistic for all sensors in comparison to the real back azimuth. We conclude that the five tested blueSeis-3A rotational sensors, when compared with respect to coherent noise, self-noise, and source direction, provide reliable and consistent results. Hence, field experiments with single rotational sensors can be undertaken.