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Strengthened inequalities for the mean width and the -norm of origin symmetric convex bodies
Get PDFBarthe, Schechtman and Schmuckenschläger proved that the cube maximizes the mean width of symmetric convex bodies whose John ellipsoid (maximal volume ellipsoid contained in the body) is the Euclidean unit ball, and the regular crosspolytope minimizes the mean width of symmetric convex bodies whose Löwner ellipsoid is the Euclidean unit ball. Here we prove close-to-be optimal stronger stability versions of these results, together with their counterparts about the -norm based on Gaussian integrals. We also consider related stability results for the mean width and the -norm of the convex hull of the support of even isotropic measures on the unit sphere
Impact of different pore types on the tensile and fatigue properties of AlSi10Mg parts produced by laser powder bed fusion
Get PDFThe mechanical properties of laser powder bed fusion (PBF-LB) parts such as fatigue strength or tensile strength are greatly influenced by the porosity occurring in the material. While it is common to achieve relative densities over 99.5% for the aluminum alloy AlSi10Mg, in practice, the formation of statistically occurring oxide pores cannot be avoided completely. Due to their irregular shape, these pores are detrimental for the mechanical properties. It is hypothesized that oxide pores can be avoided by increasing the energy input. This in turn leads to the formation of spherical keyhole pores. However, it is not sufficiently understood how keyhole porosity affects the mechanical properties with regard to oxide pores. Therefore, this work investigates the impact of keyhole pores, lack of fusion pores and oxide pores on the mechanical properties of PBF-LB AlSi10Mg parts. Results show the importance of single critical defects for the fatigue behavior compared to the impact of relative porosity and reveal the less detrimental nature of keyhole pores compared to oxide pores. Despite a much higher relative porosity, the tensile strength of specimens containing keyhole pores is comparable to that of highly dense parts whereas the fatigue strength has even improved
Frequency-Dependent Variations of the Antenna Reflection Coefficient Due to Different Wetness Conditions on the Antenna Radome
Get PDFIt is investigated how the antenna reflection coefficient (ARC) of directional radio link antennas changes over time when the antenna radome gets wet and dries off afterwards. A hand sprayer is used to deposit droplets on antenna radomes manually. Next, ARC measurements are performed repeatedly using a vector network analyzer and the ARC variation over time and frequency during the drying process is recorded into consecutive frequency response data sets. These vividly demonstrate a continuous drift from the altered, i.e., wet state back to the initial dry state. Previous work has shown that the ARC is a very useful reference for determining the wet antenna attenuation (WAA) that occurs during and also after rain events. It did, however, not explain the observed significant differences in the relation between ARC and WAA with different antennas and at individual frequencies. Inverse Fourier transforms of the recorded frequency domain ARC show a clustered concentration of variations within the unambiguous range in the near vicinity of the antenna. This supports the assumption that the changes in ARC are exclusively caused by moisture on the radome. Our findings match with measurements from previous investigations, reaffirm the expedient value of ARC to support WAA estimates, and explain why different antennas exhibit very different ARC-WAA relations
Exploring AI adoption in manufacturing: An empirical study on effects of AI readiness
Get PDFDespite the promising potential of Artificial Intelligence (AI) in manufacturing, many companies remain hesitant
to fully embrace this transformative technology, casting doubt on their preparedness for AI integration. While
previous research has initiated the exploration of the relationship between AI readiness and AI adoption,
empirical analyses in this domain are still scarce. To bridge this gap, we investigate how firms technological and
organisational AI readiness, individually and in combination, influence the adoption of AI in manufacturing
companies. Leveraging extensive empirical data from the German Manufacturing Survey, encompassing 1334 firms, we employ both descriptive and multivariate analysis. Our findings demonstrate that companies need to cultivate a robust AI readiness across both technological and organisational dimensions to facilitate successful AI adoption. Nevertheless, our approach unveils a gap between AI readiness and actual AI adoption: while manufacturing companies appear to have considerable levels of AI readiness, they are still reluctant to successfully implement AI in production processes. The results also show that companies are pursuing different strategies in the development of AI capabilities. Moreover, our analysis uncovers significant disparities among firms, highlighting the crucial role of certain firm-specific characteristics for AI adoption. Particularly interesting is our result about the u-shaped relationship between the company size and AI adoption as well as the relevance of the product complexity
Molecular‐Metallic Binding Characteristics of the Intermetalloid f‐/p‐Block Cluster [(La@In₂Bi₁₁)₂Bi₂]⁶⁻
Get PDFMain goals of contemporary research in chemistry are to create new materials with unique properties and to understand the chemical bonding in them, especially between metal atoms in larger structures. The isolation of a single lanthanide atom in a In/Bi cage offers a non-standard bonding situation, which deserves thorough exploration. In this study, the bonding behavior of La, In, and Bi atoms in the ternary cluster [(La@In2Bi11)2Bi2]6− and the complex [La(C5Me4H)3] used for its synthesis are characterized and compared by applying high energy resolution X-ray spectroscopy and computations. A clearly detectable covalent La(5d)─Bi(6p) interaction, induced in a highly electron-rich environment, is illustrated. The electronic structure of the La atom can be described as having the character of an ion being trapped and bonded in a heterometallic Bi/In cage. The advanced X-ray spectroscopic experimental tools applied here enable comparative studies of binding properties, focusing on different metals within the intermetalloid cluster. These tools can be employed iteratively to support the development of synthetic strategies that aim at tuning bond characteristics at the boundary of covalent and metallic bonding, thereby advancing the chemical and physical properties of novel multinary cluster compounds. The results were corroborated by GW and Bethe–Salpeter-equation (GW-BSE) calculations
The Dynamics of the Structure of Composite Electrodes during their Operation in Lithium‐Ion Batteries
Get PDFMechanical and electrochemical experiments are used to infer changes in composite electrodes during cell operation. Macroscopic stress levels are determined by operando substrate curvature measurements of LiFePO4 (LFP) electrodes containing different types of binders. A reduction in the stress oscillation within a few cycles indicates that the benefits of calendering can be quickly diminished due to structural changes in the electrode. The use of a relatively stiff PAA binder allows for detailed observations of the phase transition during (de)lithiation of LFP, while softer, more viscous binders lead to significantly reduced and blurred macroscopic stresses. To separate mechanical from electrochemical contributions to the electrode mechanics, electrodes are tested with a stress-controlled compression setup, which mimics the macroscopic stress states during electrochemical cycling. This experiment reveals differences in the evolution of strain and electrode resistance, which are consequences of different particle rearrangement processes. Their motion is linked to the mechanical properties of the binder, which highlights its decisive role in the resulting mechanical and time-dependent properties of composite electrodes. The results of this work demonstrate that composite electrodes cannot be considered stationary during operation. Electrodes structurally change and develop towards “steady-state” configurations depending on the operating conditions and the externally imposed load
Infilling of missing rainfall radar data with a memory-assisted deep learning approach
Get PDFIncomplete spatiotemporal meteorological observations can result in misinterpretations of the current climate state, uncertainties in early warning systems, or inaccuracies in nowcasting models and can thereby pose significant challenges in hydrology research or similar applications. Traditional statistical methods for infilling missing precipitation data demand substantial computational resources and fail over large areas with sparse data - like temporary outages of weather radars. Although recent machine learning advancements have shown promise in addressing missing meteorological or satellite observations, they typically focus on spatial aspects, overlooking the complex spatiotemporal variability characteristic of precipitation, especially during extreme events. We propose a deep convolutional neural network enhanced with a memory component to better account for temporal changes in precipitation fields. This approach can analyse arbitrary sequences from before and/or after the incomplete observation of interest. Our model is trained and evaluated on the hourly RADKLIM dataset, which features 1 km resolution precipitation data derived from combined radar and weather stations across Germany. By infilling both artificial and actual data gaps of RADKLIM, we demonstrate the model\u27s effectiveness, providing detailed insights into its capabilities during significant rainfall events, such as those in May 2012 and July 2021, including those responsible for the Ahrtal flood. This novel approach represents a step forward in hydrological applications, potentially improving the way we predict and manage water-related events by increasing the accuracy and reliability of precipitation data analysis
Discursive Polarisation and the (Non-)Binary Spectrum: Social Media Debate on Gender Diversity
Get PDFIn der Studie wird untersucht, wie soziale Medien Debatten über Geschlechterbinarität und -nonbinarität verstärken und zu ideologischer sowie affektiver Polarisierung beitragen.
Das Ziel besteht darin, sprachliche und diskursive Polarisierungsmechanismen im deutschen Social-Media-Diskurs zur Geschlechterdebatte auf Plattformen wie YouTube, Reddit, Instagram, X und Facebook zu analysieren. Mithilfe von Biterm Topic Modelling und qualitativer Kodierung wurden drei zentrale Diskursmuster identifiziert.
das binäre Geschlechtermodell,
die Gegenüberstellung biologischer und sozialer Konzepte von Geschlecht sowie
epistemische Rahmen und wissenschaftliche Kontroversen.
Zu den rhetorischen Strategien zählen unter anderen abwertende Sprache, die Berufung auf moralische oder wissenschaftliche Autoritäten sowie Vereinfachungen durch Dichotomisierungen. Zukünftige Analysen widmen sich vor allem der affektiven Polarisierung, um aufzuzeigen, wie Gruppen im Diskurs kommunikativ konstruiert werden. Methodisch leistet die Arbeit einen Beitrag durch die Kombination von computergestützter Textanalyse und diskursanalytischen Verfahren