What does the public know about technological solutions for achieving carbon neutrality? Citizens' knowledge of energy transition and the role of media. - In: Frontiers in communication, ISSN 2297-900X, Bd. 8 (2023), 1005603, S. 01-13
The present study explores the relation between media use and knowledge in the context of the energy transition. To identify relevant knowledge categories, we relied on the expertise of an interdisciplinary research team. Based on this expertise, we identified awareness-knowledge of changes in the energy system and principles-knowledge of hydrogen as important knowledge categories. With data obtained from a nationwide online survey of the German-speaking population (n = 2,025) conducted in August 2021, we examined the level of knowledge concerning both categories in the German population. Furthermore, we studied its associations with exposure to journalistic media and direct communication from non-media actors (e.g., scientists). Our results revealed a considerable lack of knowledge for both categories. Considering the media variables, we found only weak, and in some cases even negative, relations with the use of journalistic media or other actors that spread information online. However, we found comparably strong associations between both knowledge categories and the control variables of sex, education, and personal interest. We use these results to open up a general discussion of the role of the media in knowledge acquisition processes.
https://www.frontiersin.org/articles/10.3389/fcomm.2023.1005603
The 3D flower-like MnV12O31&hahog;10H2O as a high-capacity and long-lifespan cathode material for aqueous zinc-ion batteries. - In: Small structures, ISSN 2688-4062, Bd. 4 (2023), 11, 2300136, S. 1-11
Selecting the right cathode material is a key component to achieving high-energy and long-lifespan aqueous zinc-ion batteries (AZIBs); however, the development of cathode materials still faces serious challenges due to the high polarization of Zn2+. In this work, MnV12O31&hahog;10H2O (MnVO) synthesized via a one-step hydrothermal method is proposed as a promising cathode material for AZIBs. Because the stable layered structure and hieratical morphology of MnVO provide a large layer space for rapid ion transports, this material exhibits high specific capacity (433 mAh g−1 at 0.1 A g−1), an outstanding long-term cyclability (5000 cycles at a current density of 3 A g−1), and an excellent energy density (454.65 Wh kg−1). To illustrate the intercalation mechanism, ex situ X-Ray diffraction, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy are adopted, uncovering an H+/Zn2+ dual-cation co-intercalation processes. In addition, density-functional theory calculation analysis shows that MnVO has a delocalized electron cloud and the diffusion energy barrier of Zn2+ in MnVO is low, which promotes the Zn2+ transport and consequently improves the reversibility of the battery upon deep cycling. The key and enlightening insights are provided in the results for designing high-performance vanadium-oxide-based cathode materials for AZIBs.
https://doi.org/10.1002/sstr.202300136
Sex-related patterns in the electroencephalogram and their relevance in machine learning classifiers. - In: Human brain mapping, ISSN 1097-0193, Bd. 44 (2023), 14, S. 4848-4858
Deep learning is increasingly being proposed for detecting neurological and psychiatric diseases from electroencephalogram (EEG) data but the method is prone to inadvertently incorporate biases from training data and exploit illegitimate patterns. The recent demonstration that deep learning can detect the sex from EEG implies potential sex-related biases in deep learning-based disease detectors for the many diseases with unequal prevalence between males and females. In this work, we present the male- and female-typical patterns used by a convolutional neural network that detects the sex from clinical EEG (81% accuracy in a separate test set with 142 patients). We considered neural sources, anatomical differences, and non-neural artifacts as sources of differences in the EEG curves. Using EEGs from 1140 patients, we found electrocardiac artifacts to be leaking into the supposedly brain activity-based classifiers. Nevertheless, the sex remained detectable after rejecting heart-related and other artifacts. In the cleaned data, EEG topographies were critical to detect the sex, but waveforms and frequencies were not. None of the traditional frequency bands was particularly important for sex detection. We were able to determine the sex even from EEGs with shuffled time points and therewith completely destroyed waveforms. Researchers should consider neural and non-neural sources as potential origins of sex differences in their data, they should maintain best practices of artifact rejection, even when datasets are large, and they should test their classifiers for sex biases.
https://doi.org/10.1002/hbm.26417
Triangle-Mesh-Rasterization-Projection (TMRP): an algorithm to project a point cloud onto a consistent, dense and accurate 2D raster image. - In: Sensors, ISSN 1424-8220, Bd. 23 (2023), 16, 7030, S. 1-28
The projection of a point cloud onto a 2D camera image is relevant in the case of various image analysis and enhancement tasks, e.g., (i) in multimodal image processing for data fusion, (ii) in robotic applications and in scene analysis, and (iii) for deep neural networks to generate real datasets with ground truth. The challenges of the current single-shot projection methods, such as simple state-of-the-art projection, conventional, polygon, and deep learning-based upsampling methods or closed source SDK functions of low-cost depth cameras, have been identified. We developed a new way to project point clouds onto a dense, accurate 2D raster image, called Triangle-Mesh-Rasterization-Projection (TMRP). The only gaps that the 2D image still contains with our method are valid gaps that result from the physical limits of the capturing cameras. Dense accuracy is achieved by simultaneously using the 2D neighborhood information (rx,ry) of the 3D coordinates in addition to the points P(X,Y,V). In this way, a fast triangulation interpolation can be performed. The interpolation weights are determined using sub-triangles. Compared to single-shot methods, our algorithm is able to solve the following challenges. This means that: (1) no false gaps or false neighborhoods are generated, (2) the density is XYZ independent, and (3) ambiguities are eliminated. Our TMRP method is also open source, freely available on GitHub, and can be applied to almost any sensor or modality. We also demonstrate the usefulness of our method with four use cases by using the KITTI-2012 dataset or sensors with different modalities. Our goal is to improve recognition tasks and processing optimization in the perception of transparent objects for robotic manufacturing processes.
https://doi.org/10.3390/s23167030
Ni/Al multilayer reactions on nanostructured silicon substrates. - In: Journal of materials science, ISSN 1573-4803, Bd. 58 (2023), 31, S. 12811-12826
Fast energy release, which is a fundamental property of reactive multilayer systems, can be used in a wide field of applications. For most applications, a self-propagating reaction and adhesion between the multilayers and substrate are necessary. In this work, a distinct approach for achieving self-propagating reactions and adhesion between deposited Ni/Al reactive multilayers and silicon substrate is demonstrated. The silicon surface consists of random structures, referred to as silicon grass, which were created by deep reactive ion etching. Using the etching process, structure units of heights between 8 and 13 µm and density between 0.5 and 3.5 structures per µm^2 were formed. Ni and Al layers were alternatingly deposited in the stoichiometric ratio of 1:1 using sputtering, to achieve a total thickness of 5 µm. The analysis of the reaction and phase transformation was done with high-speed camera, high-speed pyrometer, and X-ray diffractometer. Cross-sectional analysis showed that the multilayers grew only on top of the silicon grass in the form of inversed cones, which enabled adhesion between the silicon grass and the reacted multilayers. A self-propagating reaction on silicon grass was achieved, due to the thermally isolating air pockets present around these multilayer cones. The velocity and temperature of the reaction varied according to the structure morphology. The reaction parameters decreased with increasing height and decreasing density of the structures. To analyze the exact influence of the morphology, further investigations are needed.
https://doi.org/10.1007/s10853-023-08794-9
A hybrid patch decomposition approach to compute an enclosure for multi-objective mixed-integer convex optimization problems. - In: Mathematical methods of operations research, ISSN 1432-5217, Bd. 0 (2023), 0, insges. 30 S.
In multi-objective mixed-integer convex optimization, multiple convex objective functions need to be optimized simultaneously while some of the variables are restricted to take integer values. In this paper, we present a new algorithm to compute an enclosure of the nondominated set of such optimization problems. More precisely, we decompose the multi-objective mixed-integer convex optimization problem into several multi-objective continuous convex optimization problems, which we refer to as patches. We then dynamically compute and improve coverages of the nondominated sets of those patches to finally combine them to obtain an enclosure of the nondominated set of the multi-objective mixed-integer convex optimization problem. Additionally, we introduce a mechanism to reduce the number of patches that need to be considered in total. Our new algorithm is the first of its kind and guaranteed to return an enclosure of prescribed quality within a finite number of iterations. For selected numerical test instances we compare our new criterion space based approach to other algorithms from the literature and show that much larger instances can be solved with our new algorithm.
https://doi.org/10.1007/s00186-023-00828-x
Data-informed reservoir computing for efficient time-series prediction. - In: Chaos, ISSN 1089-7682, Bd. 33 (2023), 7, 073109, S. 073109-1-073109-11
We propose a new approach to dynamical system forecasting called data-informed-reservoir computing (DI-RC) that, while solely being based on data, yields increased accuracy, reduced computational cost, and mitigates tedious hyper-parameter optimization of the reservoir computer (RC). Our DI-RC approach is based on the recently proposed hybrid setup where a knowledge-based model is combined with a machine learning prediction system, but it replaces the knowledge-based component by a data-driven model discovery technique. As a result, our approach can be chosen when a suitable knowledge-based model is not available. We demonstrate our approach using a delay-based RC as the machine learning component in conjunction with sparse identification of nonlinear dynamical systems for the data-driven model component. We test the performance on two example systems: the Lorenz system and the Kuramoto-Sivashinsky system. Our results indicate that our proposed technique can yield an improvement in the time-series forecasting capabilities compared with both approaches applied individually, while remaining computationally cheap. The benefit of our proposed approach, compared with pure RC, is most pronounced when the reservoir parameters are not optimized, thereby reducing the need for hyperparameter optimization.
https://doi.org/10.1063/5.0152311
3D passive microfluidic valves in silicon and glass using grayscale lithography and reactive ion etching transfer. - In: Microfluidics and nanofluidics, ISSN 1613-4990, Bd. 27 (2023), 8, 55, S. 1-12
A fabrication strategy for high-efficiency passive three-dimensional microfluidic valves with no mechanical parts fabricated in silicon and glass substrates is presented. 3D diffuser-nozzle valve structures were produced and characterized in their added value in comparison to conventional diffuser-nozzle valve designs with rectangular cross sections. A grayscale lithography approach for 3D photoresist structuring combined with a proportional transfer by reactive ion etching allowed to transfer 3D resist valve designs with high precision into the targeted substrate material. The efficiency with respect to the rectification characteristics or so-called diodicity of the studied valve designs is defined as the ratio of the pressure drops in backward and forward flow directions. The studied valve designs were characterized experimentally as well as numerically based on finite element simulations with overall matching results that demonstrate a significantly improved flow rectification of the 3D valves compared to the corresponding conventional structure. Our novel 3D valve structures show, for instance, even without systematic optimization a measured diodicity of up to 1.5 at low flow rates of only about 10 μl/s.
https://doi.org/10.1007/s10404-023-02663-2
MLflow2PROV: extracting provenance from machine learning experiments. - In: Proceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning (DEEM), (2023), 9, insges. 4 S.
Supporting iterative and explorative workflows for developing machine learning (ML) models, ML experiment management systems (ML EMSs), such as MLflow, are increasingly used to simplify the structured collection and management of ML artifacts, such as ML models, metadata, and code. However, EMSs typically suffer from limited provenance capabilities. As a consequence, it is hard to analyze provenance information and gain knowledge that can be used to improve both ML models and their development workflows. We propose a W3C-PROV-compliant provenance model capturing ML experiment activities that originate from Git and MLflow usage. Moreover, we present the tool MLflow2PROV that extracts provenance graphs according to our model, enabling querying, analyzing, and further processing of collected provenance information.
https://doi.org/10.1145/3595360.3595859
Processing-in-Memory for databases: query processing and data transfer. - In: 19th International Workshop on Data Management on New Hardware, (DaMoN 2023), June 19th 2023, (2023), S. 107-111
The Processing-in-Memory (PIM) paradigm promises to accelerate data processing by pushing down computation to memory, reducing the amount of data transfer between memory and CPU, and - in this way - relieving the CPU from processing. Particularly, in in-memory databases memory access becomes a performance bottleneck. Thus, PIM seems to offer an interesting solution for database processing. In this work, we investigate how commercially available PIM technology can be leveraged to accelerate query processing by offloading (parts of) query operators to memory. Furthermore, we show how to address the problem of limited PIM storage capacity by interleaving transfer and computation and present a cost model for the data placement problem.
https://doi.org/10.1145/3592980.3595323