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To date, no AM technology for large-volume metallic components has been established on an industrial scale like Laser Powder Bed Fusion (LPBF). This contribution presents a new process approach to fabricate larger metallic structures from steel by inductive melting of a continuously fed wire. The presented method is characterized by the comparatively low energy input in contrast to comparable processes for AM, such as arc, laser, or electron beam. Previously published work is based on the principle of a melt reservoir of low-melting alloys and droplet delivery using gas pressure or vibration. In contrast, the approach described here is to transfer the material directly from the end of the wire to the substrate by continuous drop deposition. However, to avoid a stochastic material transition, a suitable process strategy has to be chosen. This challenge is met by a pulsed coil current and the resulting periodically varying Lorentz forces in order to achieve regular and forced droplet detachment. Tests confirm the principle of controlled droplet detachment and reveal the great potential of this technological approach.

In situ mass spectrometry measurements of FAI degradation for a deeper understanding of perovskite co-evaporation and its process parameters.

The relaxation of the above-gap ("hot") carriers in lead halide perovskites (LHPs) is important for applications in photovoltaics and offers insights into carrier-carrier and carrier-phonon interactions. However, the role of quantum confinement in the hot carrier dynamics of nanosystems is still disputed. Here, we devise a single approach, ultrafast pump- push-probe spectroscopy, to study carrier cooling in six different size-controlled LHP nanomaterials. In cuboidal nanocrystals, we observe only a weak size effect on the cooling dynamics. In contrast, two-dimensional systems show suppression of the hot phonon bottleneck effect common in bulk perovskites. The proposed kinetic model describes the intrinsic and density-dependent cooling times accurately in all studied perovskite systems using only carrier-carrier, carrier-phonon, and excitonic coupling constants. This highlights the impact of exciton formation on carrier cooling and promotes dimensional confinement as a tool for engineering carrier-phonon and carrier-carrier interactions in LHP optoelectronic materials., ACS Nano, 17 (7), ISSN:1936-0851, ISSN:1936-086X

For automated driving, the perception provided by lidar, radar, and camera sensors is safety-critical. Validating sensor perception reliability with standard empirical tests is impractical, owing to the large required test effort and the need for a reference truth to identify sensor errors. To address these challenges, we investigate the possibility of estimating sensor perception reliability without a reference truth. In particular, we propose a framework to learn sensor perception reliability solely by exploiting sensor redundancies. We formulate a likelihood function for redundant binary sensor data without a reference truth and propose a Gaussian copula to model dependent sensor errors. Synthetic numerical experiments show that under an adequate dependence model, correct sensor perception reliabilities can be estimated without a reference truth. Because the selection of an adequate dependence model is challenging without a reference truth, we also investigate how inadequate dependence models influence the estimation. The proposed framework is a step toward the validation of sensor perception reliability because it could enable the learning of reliabilities from a fleet of driver-controlled vehicles equipped with series sensors.

Data based materials science is the new promise to accelerate materials design. Especially in computational materials science, data generation can easily be automatized. Usually, the focus is on processing and evaluating the data to derive rules or to discover new materials, while less attention is being paid on the strategy to generate the data. In this work, we show that by a sequential design of experiment scheme, the process of generating and learning from the data can be combined to discover the relevant sections of the parameter space. Our example is the energy of grain boundaries as a function of their geometric degrees of freedom, calculated via atomistic simulations. The sampling of this grain boundary energy space, or even subspaces of it, represents a challenge due to the presence of deep cusps of the energy, which are located at irregular intervals of the geometric parameters. Existing approaches to sample grain boundary energy subspaces therefore either need a huge amount of datapoints or a~priori knowledge of the positions of these cusps. We combine statistical methods with atomistic simulations and a sequential sampling technique and compare this strategy to a regular sampling technique. We thereby demonstrate that this sequential design is able to sample a subspace with a minimal amount of points while finding unknown cusps automatically.

Thermal evaporation is a well-established and versatile method for the deposition of large-area, uniform, high-quality semiconducting layers in a broad range of optoelectronic applications. Despite extensive investigation of deposition by thermal evaporation in the field of perovskite solar cells, solution-processed perovskite devices still significantly outperform those fabricated by thermal evaporation, particularly in the case of methylammonium-free perovskite compositions. Herein, we fabricate and investigate thermally evaporated Cs0.1FAxPbI2+xBr0.1 perovskite solar cells and explore the effects of FAI deficiency or excess and that of post-annealing on the perovskite layer properties and device performance. We find that annealing can significantly improve the optical and structural properties of FAI-poor perovskite layers, resulting in a stark enhancement of their photovoltaic performance. While stoichiometric devices are also improved, albeit to a lesser degree, by post-annealing, this process is found to be detrimental for the FAI-rich devices, resulting in a drastic loss of performance. We show that annealed stoichiometric devices with an optimised active layer thickness result in power conversion efficiencies of up to 16.6%, approaching the performance of solution-processed devices of similar composition.

Recent progress on perovskite lasers has shown the large potential for such materials to become a basis for commercially available microlasers in the near feature. Herein, distributed feedback (DFB) lasers based on vacuum‐processed CsPbBr 3 are investigated. The expansion of the DFB structure from 1D to 2D suppresses parasitic amplified spontaneous emission (ASE), resulting in the multiple‐time‐enhanced lasing output. Further, the photoluminescence and lasing behavior of 1D and 2D DFB structures are explored through the k‐space imaging analysis.

We revisit the classical problem of nonparametric density estimation but impose local differential privacy constraints. Under such constraints, the original multivariate data $X_1,\ldots,X_n \in \mathbb{R}^d$ cannot be directly observed, and all estimators are functions of the randomised output of a suitable privacy mechanism. The statistician is free to choose the form of the privacy mechanism, and in this work we propose to add Laplace distributed noise to a discretisation of the location of a vector $X_i$. Based on these randomised data, we design a novel estimator of the density function, which can be viewed as a privatised version of the well-studied histogram density estimator. Our theoretical results include universal pointwise consistency and strong universal $L_1$-consistency. In addition, a convergence rate over classes of Lipschitz functions is derived, which is complemented by a matching minimax lower bound. We illustrate the trade-off between data utility and privacy by means of a small simulation study.

We consider the nonparametric estimation of the intensity function of a Poisson point process in a circular model from indirect observations $$N_1,\ldots ,N_n$$ . These observations emerge from hidden point process realizations with the target intensity through contamination with additive error. In case that the error distribution can only be estimated from an additional sample $$Y_1,\ldots ,Y_m$$ we derive minimax rates of convergence with respect to the sample sizes n and m under abstract smoothness conditions and propose an orthonormal series estimator which attains the optimal rate of convergence. The performance of the estimator depends on the correct specification of a dimension parameter whose optimal choice relies on smoothness characteristics of both the intensity and the error density. We propose a data-driven choice of the dimension parameter based on model selection and show that the adaptive estimator attains the minimax optimal rate.

We consider the estimation of quadratic functionals in a Gaussian sequence model where the eigenvalues are supposed to be unknown and accessible through noisy observations only. Imposing smoothness assumptions both on the signal and the sequence of eigenvalues, we develop a minimax theory for this problem. We propose a truncated series estimator and show that it attains the optimal rate of convergence if the truncation parameter is chosen appropriately. Consequences for testing problems in inverse problems are equally discussed: in particular, the minimax rates of testing for signal detection and goodness-of-fit testing are derived., Comment: Corrected some typos

The use of digital solutions and artificial intelligence (AI) in corporate education and training is analyzed in this article using the example of an agile IT-supported learning concept. The focus is on the preliminary findings of the EU project “Career 4.0”, including the findings from a usability evaluation of a European learning platform. The central role of constructive feedback processes and user-friendliness for the success of collaborative and self-organized learning and the mentoring process are discussed. At the same time, it is shown which potentials and resistances can occur for the different user groups when using the platform and how these effects can be strengthened or weakened by AI tools in the future.

The new technical developments in the context of digitization and artificial intelligence offer a multitude of design and innovation potentials, whereby their utilization represents the central challenge regarding the digital transformation in the companies and the economy of the single EU countries. In order to face these challenges, it is necessary for companies and their employees to acquire new competences so that uncertainties regarding individual and organizational resistances and potentials as well as the interplay between technology, organization and people can effectively and professionally be dealt with. In the future, Digital Coaches could advise companies on the competences that companies and their employees should have in order to shape the digital transformation. They could also advise companies on how to establish and use specific learning environments so that organizational members can acquire the necessary competences. This is being worked out in the EU project "Digital Coach", whereby the established maturity model "ADAPTION" is used to reveal digital competence potentials as well as deficits. With the concept of the learning factory or the concept of the teaching factory the competences that are relevant for the digital transformation are further developed. The aim of the project is therefore to develop a certificate programme for Digital Coaches as well as quality standards for learning factories.

We consider non-parametric density estimation in the framework of local, both pure and approximate, differential privacy. In contrast to centralized privacy scenarios with a trusted curator, in the local setup anonymization must be guaranteed already on the individual data owners’ side and must therefore precede any data mining tasks. Thus, the published anonymized data should be compatible with as many statistical procedures as possible. We consider different mechanisms to establish pure and approximate differential privacy, respectively. We obtain minimax type results over Sobolev classes indexed by a smoothness parameter s>1∕2 for the mean squared error at a fixed point. In particular, we show that appropriately defined kernel density estimators can attain the optimal rate of convergence if the bandwidth parameter is correctly specified. Notably, the optimal convergence rate in terms of the sample size n is n−(2s−1)∕(2s+1) under pure differential privacy and thus deteriorated to the rate n−(2s−1)∕(2s) which holds both without privacy restrictions and under approximate differential privacy. Since the optimal choice of the bandwidth parameter depends on the smoothness s and is thus not accessible in practise, adaptive methods for bandwidth selection are necessary and must, in the local privacy framework, be performed based on the anonymized data only. We address this problem by means of variants of Lepski’s method tailored to the privacy setups at hand and obtain general oracle inequalities for private kernel density estimators. In the Sobolev case, the resulting adaptive estimators attain the optimal rates of convergence at least up to logarithmic factors. On the side, we discuss some critical issues related with the notion of approximate differential privacy.

Metal halide perovskites are the first solution processed semiconductors that can compete in their functionality with conventional semiconductors, such as silicon. Over the past several years, perovskite semiconductors have reported breakthroughs in various optoelectronic devices, such as solar cells, photodetectors, light emitting and memory devices, and so on. Until now, perovskite semiconductors face challenges regarding their stability, reproducibility, and toxicity. In this Roadmap, we combine the expertise of chemistry, physics, and device engineering from leading experts in the perovskite research community to focus on the fundamental material properties, the fabrication methods, characterization and photophysical properties, perovskite devices, and current challenges in this field. We develop a comprehensive overview of the current state-of-the-art and offer readers an informed perspective of where this field is heading and what challenges we have to overcome to get to successful commercialization. published

Organisationen stehen unter dem Eindruck mit folgenden Herausforderungen konfrontiert zu werden: Die Innovationsgeschwindigkeit hat sich in den letzten Jahren kontinuierlich erhoht. Die Zahl der technologischen Neuentwicklungen nimmt zu. Gleichzeitig steigt der Druck diese aufzugreifen, um die Uberlebens- und Wettbewerbsfahigkeit ihrer Organisation zu ermoglichen und zu gewahrleisten.

Considering the demand for low temperature bonding processes in packaging of micro-electro-mechanical systems (MEMS), such as accelerometers or micro mirrors, in this paper a method for selective and energy-efficient heating of metallic intermediate bonding layers by applying high-frequency electromagnetic fields with frequencies in the range of 1. 0 < f < 2.0 MHz is presented. Therefore, the induction heating process was modeled and analyzed by means of numerical simulation. Additionally, heating experiments were carried out by using IR thermography in order to verify the simulation results. The developed inductor shape allows almost homogeneous heating of all metallic layers with heating rates of more than DT∕Dt = 150 K∕s.

Bisher wurde herausgearbeitet, was die kennzeichnenden Merkmale der Implementierungsproblematik sind, von welcher wissenschaftstheoretischen Basis und welchem Kompetenzverstandnis in der vorliegenden Arbeit ausgegangen wird. Nun stellt sich die Frage, welche Forschungsergebnisse es bisher im Hinblick auf die Implementierung von praxisorientierten Konzepten gibt. Beispielhaft wird auf die folgenden drei Forschungsfelder eingegangen, die sich mit der Implementierungsproblematik auseinandersetzen: Forschung zum Qualitatsmanagement, zur Strategieimplementierung und zur Implementierung von (neuen) Informationssystemen.

Ziel der nachfolgenden Ausfuhrungen ist es, die zentralen Bausteine fur eine Theorie der Reflexion, und darauf aufbauend die der Reflexionskompetenz, herauszuarbeiten. Reflexion wird als zielgerichtetes Nachdenken interpretiert werden. Intention der Reflexion ist es, Entwicklungsprozesse zu initiieren und nicht in erster Linie oder ausschlieslich Probleme zu losen (Wyss, 2013).

The article discusses which central importance is attached to reflection and reflection competence based on current discussions about and the use of agile management methods. The focus is on the implementation of instruments to promote innovation and improve quality. In this context, the different forms of organizational cultures play a determining role. Against this background, the results of an empirical study as well as the structural model of economic didactics, economic didactic conclusions for the promotion of reflection competence are worked out regarding teaching and learning objectives, topic, methodology, addressee system and teaching and learning control.

Im Folgenden geht es darum, an den aktuellen Stand der wissenschaftlichen Diskussion innerhalb der Wirtschaftsdidaktik und der okonomischen Bildung anzuknupfen und einen Bezug zur Implementierungsproblematik herzustellen. Dabei wird auf das Verhaltnis zwischen Erziehung und Wirtschaft eingegangen, die Rolle der Lebenssituation im Hinblick auf die Curriculumentwicklung herausgearbeitet und es werden Ansatze zur Idee der Schlusselqualifikation verdeutlicht.

In der empirischen Sozialforschung ist es allgemeiner Konsens, dass es die einzig zutreffende Methode nicht gibt. Jede Methode hat, abhangig u.a. vom jeweiligen Forschungsziel, der Forschungslucke und dem Forschungsgegenstand, ihre Vor- und Nachteile. Lange Zeit dominierte in der empirischen Sozialforschung der Streit zwischen Vertretern der qualitativen und denen der quantitativen Forschung (Flick, 2011; Reichertz, 2014).

The present article focuses on an entrepreneurship education concept which enables experienced professionals to further develop their knowledge as mentors in order to provide vocational orientation to young people searching for a job against the background of digitization, thus supporting them, for example, on their way to independence. The corresponding three-year EU project “Job Developer” was funded by the European Commission. Potential aspects to improve the quality of the concept itself but also in relation to the implementation of this concept were worked out. The following article focuses on the question which quality criteria can be used to improve the quality of the implementation of the Job Developer concept with the help of specific existing or future IT-solutions. On this basis, a potential and resistance analysis was carried out during the project.

We address the problem of non-parametric density estimation under the additional constraint that only privatised data are allowed to be published and available for inference. For this purpose, we adopt a recent generalisation of classical minimax theory to the framework of local $\alpha$-differential privacy and provide a lower bound on the rate of convergence over Besov spaces $\mathcal{B}^{s}_{pq}$ under mean integrated $\mathbb{L}^{r}$-risk. This lower bound is deteriorated compared to the standard setup without privacy, and reveals a twofold elbow effect. In order to fulfill the privacy requirement, we suggest adding suitably scaled Laplace noise to empirical wavelet coefficients. Upper bounds within (at most) a logarithmic factor are derived under the assumption that $\alpha$ stays bounded as $n$ increases: A linear but non-adaptive wavelet estimator is shown to attain the lower bound whenever $p\geq r$ but provides a slower rate of convergence otherwise. An adaptive non-linear wavelet estimator with appropriately chosen smoothing parameters and thresholding is shown to attain the lower bound within a logarithmic factor for all cases.

This article contains findings of simulation research on longitudinal induction welding of cladded pipes with use of simultaneous double frequency. Solutions are proposed to reach the required temperature distribution at the welding edge for the cladding composite of S355 and Alloy 625 with single and simultaneous double frequency. An advanced consideration of magnetic and other material properties was performed to simulate the dominating physical effects of high frequency (HF) welding. The background of use and advantages of simultaneous double frequency are presented. In the context of the research, a correlation for welding speed, frequency and temperature distribution with industrial relevance was found for the cladded pipe welding.