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Digitalkompetenz und ein „Growth Mindset“ gelten als begehrte Ressourcen auf der „People Side of Digitization“ – nicht nur im häufig akademisch geprägten Management, sondern auch unter Nachwuchsführungskräften. Wie derartige „Future Skills“ trainiert und vermittelt werden können, wird vor dem Hintergrund des Fachkräftemangels zur Aufgabe von strategischer Bedeutung. Als Hoffnungsträger gelten Weiterbildungsformate. Während Weiterbildung jedoch zur Vervielfältigung von Routinewissen und für Anpassungsqualifizierungen mit dem Ziel der Effizienzsteigerung bewährt ist, sind Möglichkeiten und Grenzen für die Vermittlung von Future Skills weniger erforscht. Vor diesem Hintergrund werden im Rahmen dieses Forschungsberichts Ergebnisse und Erfahrungen aus einer Fallstudie diskutiert. Empirischer Kontext ist die Entwicklung und Evaluation eines Blended-Learning-Weiterbildungsformats „Digitalkompetenz“ für die Zielgruppe Führungskräftenachwuchs. Welche Effekte für Digitalkompetenz sind zu erzielen? Welche Gestaltungsideen kommen in Betracht?
Directed Energy Deposition-Laser (DED-L) enables high-performance coatings through melting and successive powder deposition. Its compositional flexibility suits functionally graded layers that enhance corrosion and wear resistance. This study aimed to improve parameters for producing dense, defect-free, graded Ni- and Fe-based coatings by varying the scanning speed and deposition strategy (monodirectional versus bidirectional, with/without layer rotation), while keeping the power and hatch distance constant. Laser and electron microscopy were used to link parameters to porosity and uniformity. Optimal settings minimized pores, improved interlayer bonding and preserved geometry; inadequate parameters yielded porous, irregular deposits. A bidirectional path with 90° rotation appeared best. Ongoing research activities are needed to assess its properties.
Highlights
What are the main findings?
- Bidirectional samples show fewer pores than samples with monodirectional movement.
- Increasing the scanning speed leads to lower porosity (bidirectional scanning mode with 90°
layer rotation).
- Bidirectional deposition with 90° layer rotation exhibits best quality
What are the implications of the main findings?
- Systematic study of scanning speed and deposition strategy.
- Practical guidance for process improvement of graded metal coatings; properties under evaluation.
- Functionally graded Ni-Fe coatings fabricated by DED-L with tailored microstructures.
The surface of Mercury is highly space weathered, and the products of space weathering are different than on the Moon. Here we simulate the interaction of solar wind ions with the surface of Mercury by implanting low-iron-bearing (∼3 wt% Fe) olivine slices with 4 keV He+ ions both under ambient conditions and at 450 °C during the irradiation. The sample irradiated under ambient conditions shows a ∼140 nm thick partially amorphous layer on top of the grain with high concentrations of bubbles and cavities. However, the sample irradiated at 450 °C has a 110–140 nm thick polycrystalline layer with 1–5 nm sized nanoparticles of metallic iron and no bubbles or cavities. Mg atoms are preferentially sputtered from the ion-damaged layer present on the sample irradiated under ambient conditions, but no difference in composition is seen between the crystalline olivine and the ion-damaged layer in the sample irradiated at 450 °C. In addition, a thin amorphous film deposited on a micro-electromechanical systems–based chip is heated up to 1200 °C in situ inside a transmission electron microscope for a few milliseconds, and it produced nanoparticles of size 5–30 nm. Our results show that solar wind irradiation of low-iron-bearing olivine in the equatorial regions of Mercury will produce nanoparticles of iron whose sizes will be larger than that on the surface of the Moon and that the size will further increase during impacts of dust particles. Solar-wind-implanted He can diffuse out of regolith grains and can be a source of He observed in the exosphere of Mercury.
In supply chain management of port operations querying and visualizing network data often requires complex joins, nested queries and predefined reporting templates, which can hinder exploratory analysis and decision-making. When relational databases become too rigid and cumbersome for transactional processing, we envision an alternative approach using graph data models, provided by transforming entities (e.g. containers, vessels, terminals) into nodes and their relationships (e.g. arrival, loading, handling) into edges. To investigate the potential benefits of transforming relational supply chain data into a graph-based model, we designed and implemented a structured approach that integrated data processing, transformation and performance analysis across both relational and graph databases.
Der Aufsatz untersucht anhand empirischer Daten zu ca. 400 Studierenden wirtschaftsjuristischer Bachelor-Studiengänge an der Westfälischen Hochschule eine Reihe etablierter und neuer Indikatoren für den Studienerfolg. Insbesondere schulnotenbezogene Prädiktoren erweisen sich dabei als nur noch bedingt aussagefähig, andere bekannte (insbes. Motivationslevel) und neue (insbes. Berufsausbildung vor dem Studium) erweisen sich als verlässliche, wenn auch nicht starke Prädiktoren.
We extend the spin-boson model of open quantum systems to the regime of quantum 1/𝑓^𝜂 noise characterized by negative exponents of its spectral distribution. Using the numerically exact time-evolving matrix product operator, we find the dynamic regime diagram, including pseudocoherent dynamics controlled by quantum 1/𝑓^𝜂 noise. We determine the dephasing rate and find for it an empirical formula valid at zero temperature. The bath reorganization energy depends on the infrared bath cutoff frequency, revealing an increased sensitivity of the dephasing on the measurement time of an experiment. Our results apply to a qubit as an elementary building block of a quantum computer and pave the way toward a quantum treatment of low-frequency noise in more complex architectures.
Low pressure capacitively coupled radiofrequency plasmas operated in a mixture of aniline vapor and argon are used for the deposition of thin films on silicon substrates. The influence of the aniline vapor fraction in the gas mixture upon the plasma properties and the characteristics of the deposited thin film is analyzed. Plasmas diagnostics are carried out using mass spectrometry and optical emission spectroscopy and the thin films are characterized by means of Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and near-edge X-ray absorption fine-structure spectroscopy. Experiments highlight that the use of a low aniline/argon ratio leads to the deposition of an amorphous film whereas high-aniline/argon ratios allow the synthesis of a plasma polymer similar to polyaniline. The properties of such plasmas and the mechanisms involved in the deposition process are discussed in detail.
The design of novel and abundant catalytic materials for electrolysis is crucial for reaching carbon neutrality of the global energy system. A deliberate approach to catalyst design requires both theoretical and experimental knowledge not only of the target reactions but also of the supplementary mechanisms affecting the catalytic activity. In this study, we focus on the interplay of hydrogen mobility and reactivity in the hydrogen evolution reaction catalyst MoS2. We have studied the diffusion of atomic hydrogen and water by means of neutron and X-ray photoelectron spectroscopies combined with classical molecular dynamics simulations. The observed interaction of water with single-crystal MoS2 shows the possibility of intercalation within volume defects, where it can access edge sites of the material. Our surface studies also demonstrate that atomic hydrogen can be inserted into MoS2, where it then occupies various adsorption sites, possibly favoring defect vicinities. The motion of H atoms parallel to the layers of MoS2 is fast with D ≈ 1 × 10−9 m2/s at room temperature and exhibits Brownian diffusion behavior with little dependence on temperature, i.e., with a very low diffusion activation barrier.
In the burgeoning field of quantum computing, the precise design and optimization of quantum pulses are essential for enhancing qubit operation fidelity. This study focuses on refining the pulse engineering techniques for superconducting qubits, employing a detailed analysis of square and Gaussian pulse envelopes under various approximation schemes. We evaluated the effects of coherent errors induced by naive pulse designs. We identified the sources of these errors in the Hamiltonian model’s approximation level. We mitigated these errors through adjustments to the external driving frequency and pulse durations, thus implementing a pulse scheme with stroboscopic error reduction. Our results demonstrate that these refined pulse strategies improve performance and reduce coherent errors. Moreover, the techniques developed herein are applicable across different quantum architectures, such as ion-trap, atomic, and photonic systems.
The exponential growth of chemical literature necessitates the development of automated tools for extracting and curating molecular information from unstructured scientific publications into open-access chemical databases. Current optical chemical structure recognition (OCSR) and named entity recognition solutions operate in isolation, which limits their scalability for comprehensive literature curation. Here we present MARCUS (Molecular Annotation and Recognition for Curating Unravelled Structures), a tool designed for natural product literature curation that integrates COCONUT-aware schema mapping, CIP-based stereochemical validation, and human-in-the-loop structure refinement. This integrated web-based platform combines automated text annotation, multi-engine OCSR, and direct submission capabilities to the COCONUT database. MARCUS employs a fine-tuned GPT-4 model to extract chemical entities and utilises a Human-in-the-loop ensemble approach integrating DECIMER, MolNexTR, and MolScribe for structure recognition. The platform aims to streamline the data extraction workflow from PDF upload to database submission, significantly reducing curation time. MARCUS bridges the gap between unstructured chemical literature and machine-actionable databases, enabling FAIR data principles and facilitating AI-driven chemical discovery. Through open-source code, accessible models, and comprehensive documentation, the web application enhances accessibility and promotes community-driven development. This approach facilitates unrestricted use and encourages the collaborative advancement of automated chemical literature curation tools.

