Universität Paderborn

The optimization of voltage utilization can boost electric drives' power and torque capabilities, which is of particular interest in transportation applications. However, this is a weak point of most control approaches, like linear field-oriented control (FOC) methods using standard modulation schemes. This paper introduces the model predictive direct self-control (MPDSC) strategy, a modification of direct self-control (DSC) for its application in a digital implementation to achieve maximum voltage utilization (i.e., six-step operation) for permanent magnet synchronous machines. This solution is suitable for highly utilized machines with heavy magnetic (cross-)saturation and low sampling to fundamental frequency ratio. The modifications include using load angle regulation to control the selected operating point and model prediction to compensate for the actuation delay. The proposed system can achieve six-step operation with accurate torque control and robustness against disturbances and parameter estimation inaccuracies. Comprehensive simulation and experimental results demonstrate the performance of the proposed MPDSC while operating at the voltage constraint. In particular, a transient rise time of 2.3 ms to maximum torque, current reduction for equal torque-speed operating point of up to 18 %, and maximum torque increase for equal current amplitude of up to 15 % compared to conventional FOC have been empirically observed.

In this contribution the resilience of students in the computer and information literacy (CIL) domain is focused. In this context, research towards individual student resilience is of relevance in order to examine characteristics from the student level that can be used as setscrews by educators and other educational stakeholders to minimize or overcome social issues in the CIL domain. Taking advantage of the representative cross-sections of students from the International Computer and Information Literacy Study 2018 (ICILS 2018), the question of the prevalence of resilient students (research question 1), differences between educational systems in international comparison (research question 2), and students’ related antecedents and process factors from the ICILS 2018 contextual model (research question 3) have been focused via using a logistic regression approach. The sample consisted of 46,561 students aged 14 from 14 countries. Cross country analyses revealed that student’s sex and their cultural capital are the strongest predictors for resilience in the CIL domain. However, including family’s process characteristics shows that students’ self-efficacy toward the use of information and communication technology (ICT), their use of ICT for information-related activities itself and the use of ICT for basic and advanced purposes have been identified as significantly related to student resilience.

Despite recent achievements in the field of frustrated Lewis pairs (FLPs) for small molecule activations, the reversible activation and catalytic transformations of N–H-activated ammonia remain a challenge. Here we report on a rare combination of an aluminium Lewis acid and a carbon Lewis base. A so-called hidden FLP consisting of a phosphorus ylide featuring an aluminium fragment in the ortho position of a phenyl ring scaffold is introduced. Although the formation of the Lewis acid/base adduct is observed in the solid state, which at first glance leads to formally quenched FLP reactivity, we show that the title compound readily reacts with non-aqueous ammonia thermoneutrally and splits the N–H bond reversibly at ambient temperature. In addition, NH3 transfer reactions mediated by a main-group catalyst are presented. This proof-of-principle study is expected to initiate further activities in utilizing N–H-activated ammonia as a readily available, atom-economical nitrogen source.

A new approach for the characterization of CO 2 methanation catalysts prepared by thermal decomposition of a nickel MOF by hard X‐ray photon‐in/photon‐out spectroscopy in form of high energy resolution fluorescence detected X‐ray absorption near edge structure spectroscopy (HERFD‐XANES) and valence‐to‐core X‐ray emission (VtC‐XES) is presented. In contrast to conventional X‐ray absorption spectroscopy, the increased resolution of both methods allows a more precise phase determination of the final catalyst, which is influenced by the conditions during MOF decomposition.

One of the fundamental problems in shape analysis is to align curves or surfaces before computing geodesic distances between their shapes. Finding the optimal reparametrization realizing this alignment is a computationally demanding task, typically done by solving an optimization problem on the diffeomorphism group. In this paper, we propose an algorithm for constructing approximations of orientation-preserving diffeomorphisms by composition of elementary diffeomorphisms. The algorithm is implemented using PyTorch, and is applicable for both unparametrized curves and surfaces. Moreover, we show universal approximation properties for the constructed architectures, and obtain bounds for the Lipschitz constants of the resulting diffeomorphisms.

Manipulating bosonic condensates with electric fields is very challenging as the electric fields do not directly interact with the neutral particles of the condensate. Here we demonstrate a simple electric method to tune the vorticity of exciton-polariton condensates in a strong coupling liquid crystal (LC) microcavity with CsPbBr3 microplates as active material at room temperature. In such a microcavity, the LC molecular director can be electrically modulated giving control over the polariton condensation in different modes. For isotropic nonresonant optical pumping we demonstrate the spontaneous formation of vortices with topological charges of +1, +2, −2, and −1. The topological vortex charge is controlled by a voltage in the range of 1 to 10 V applied to the microcavity sample. This control is achieved by the interplay of a built-in potential gradient, the anisotropy of the optically active perovskite microplates, and the electrically controllable LC molecular director in our system with intentionally broken rotational symmetry. Besides the fundamental interest in the achieved electric polariton vortex control at room temperature, our work paves the way to micron-sized emitters with electric control over the emitted light’s phase profile and quantized orbital angular momentum for information processing and integration into photonic circuits.

For motor learning, the processing of behavioral outcomes is of high significance. The feedback‐related negativity (FRN) is an event‐related potential, which is often described as a correlate of the reward prediction error in reinforcement learning. The number of studies examining the FRN in motor tasks is increasing. This meta‐analysis summarizes the component in the motor domain and compares it to the cognitive domain. Therefore, a data set of a previous meta‐analysis in the cognitive domain that comprised 47 studies was reanalyzed and compared to additional 25 studies of the motor domain. Further, a moderator analysis for the studies in the motor domain was conducted. The FRN amplitude was higher in the motor domain than in the cognitive domain. This might be related to a higher task complexity and a higher feedback ambiguity of motor tasks. The FRN latency was shorter in the motor domain than in the cognitive domain. Given that sensory information can be used as an external feedback predictor prior to the presentation of the final feedback, reward processing in the motor domain may have been faster and reduced the FRN latency. The moderator variable analysis revealed that the feedback modality influenced the FRN latency, with shorter FRN latencies after bimodal than after visual feedback. Processing of outcome feedback seems to share basic principles in both domains; however, differences exist and should be considered in FRN studies. Future research is motivated to scrutinize the effects of bimodal feedback and other moderators within the motor domain.

We study the expressivity and the complexity of various logics in probabilistic team semantics with the Boolean negation. In particular, we study the extension of probabilistic independence logic with the Boolean negation, and a recently introduced logic FOPT. We give a comprehensive picture of the relative expressivity of these logics together with the most studied logics in probabilistic team semantics setting, as well as relating their expressivity to a numerical variant of second-order logic. In addition, we introduce novel entropy atoms and show that the extension of first-order logic by entropy atoms subsumes probabilistic independence logic. Finally, we obtain some results on the complexity of model checking, validity, and satisfiability of our logics.

Presents Hannah Arendt from a fresh angle: as a thinker who engages in both word and deed with the practices of the common world, and who invites us to do the same. The essential element of her unique manner of thinking politics, her concept of ‘exercises in political thinking,’ remains insufficiently regarded, for all the recent flurry of discussion about her work. Based on this concept, the authors uncover the deeply practical aspect of her thinking, including in works that are taken to be her most abstract and theoretical, such as The Human Condition and The Life of the Mind . Arendt is well known for her accounts of action, totalitarianism, revolution; alongside her insightful study of lying in politics and the prevalence of prejudice, to name only name a few. These aspects in her thought have gained widespread, even popular, attention in recent years. A growing number of publications on Arendt’s political theory, especially those engaging in critical analysis of current political phenomena, amply demonstrates the contemporary relevance of her theoretical concepts. Our book, however, will introduce the reader not only to Hannah Arendt’s theory and its current salience, but also to her practices of reflective judgment.

Sandwich packings represent new separation column internals, with a potential to intensify mass transfer. They comprise two conventional structured packings with different specific geometrical surface areas. In this work, the complex fluid dynamics in sandwich packings is modeled using a novel approach based on a one-dimensional, steady momentum balance of the liquid and gas phases. The interactions between the three present phases (gas, liquid, and solid) are considered by closures incorporated into the momentum balance. The formulation of these closures is derived from two fluid-dynamic analogies for the film and froth flow patterns. The adjustable parameters in the closures are regressed for the film flow using dry pressure drop measurements and liquid hold-up data in trickle flow conditions. For the froth flow, the tuning parameters are fitted to overall pressure drop measurements and local liquid hold-up data acquired from ultra-fast X-ray tomography (UFXCT). The model predicts liquid hold-up and pressure drop data with an average relative deviation of 16.4 % and 19 %, respectively. Compared to previous fluid dynamic models for sandwich packings, the number of adjustable parameters could be reduced while maintaining comparable accuracy.

In the present study, the effects of oxygen plasma treatment and subsequent 2 nm thin Al2O3 film deposition by atomic layer deposition on about 30 nm thick hexamethyldisilazane polymer layers are investigated by using a combination of spectroscopic and electrochemical analysis. The investigations focus on the microporosity of the corresponding films and their structural changes. Upon oxygen plasma treatment, the surface near region of the films is converted into SiOx, and the microporosity is increased. Atomic layer deposition of Al2O3 on the plasma oxidized films leads to the decrease of pore sizes and an effective sealing. A correlation between the film microporosity and the change of hydroxyl groups of the films with the adsorption of water was established by ellipsometric porosimetry and in situ Fourier transform infrared (FTIR) spectroscopy. Moreover, electrochemical analysis provided complementary information on the electrolyte up-take in the differently conditioned thin films.

Recent years have brought major technological breakthroughs in artificial intelligence (AI), and firms are expected to invest nearly $98 B in 2023. However, many AI projects never leave the pilot phase, and many companies have difficulties extracting value from their AI initiatives. To explain this contradiction, this article reports on a study of 55 projects implementing AI in organizations. It shows that organizational challenges in implementing AI projects are a result of a paradoxical tension created by two different perspectives on data science work: craft and mechanical work. Executives, managers, and data scientists should actively manage this tension to enable and sustain value creation through AI.

Understanding charge separation processes after photo-excitation in organic photovoltaics is of great importance for optimizing device performance. Many studies have associated a polaron-pair or intrachain charge transfer state in organic polymers with increased charge separation efficiency. It is then natural to ask how the chemical structure influences charge separation, enabling a more targeted materials design. Here, we report on non-adiabatic ab-initio molecular dynamics simulations of the hot exciton dynamics following photo-excitation for a series of donor-acceptor polymers. We provide detailed insights into Coulomb attractive energy and dynamical evolution of dipole moments in the excited states. The former is correlated with polaron-pair recombination thus preventing charge separation, the latter is a potential enabler of charge separation. We calculate the ultrafast dynamics of these relatively simple charge-separation-efficiency quantifiers, correlate them with the underlying chemical structure, and relate them to their static counterparts in statistical ensembles. The insights obtained here can be extended to more complex molecular compound scenarios and will inform future optimization of materials and device performance.

Class expression learning in description logics has long been regarded as an iterative search problem in an infinite conceptual space. Each iteration of the search process invokes a reasoner and a heuristic function. The reasoner finds the instances of the current expression, and the heuristic function computes the information gain and decides on the next step to be taken. As the size of the background knowledge base grows, search-based approaches for class expression learning become prohibitively slow. Current neural class expression synthesis (NCES) approaches investigate the use of neural networks for class expression learning in the attributive language with complement (\(\mathcal {ALC}\)). While they show significant improvements over search-based approaches in runtime and quality of the computed solutions, they rely on the availability of pretrained embeddings for the input knowledge base. Moreover, they are not applicable to ontologies in more expressive description logics. In this paper, we propose a novel NCES approach which extends the state of the art to the description logic \(\mathcal {ALCHIQ(D)}\). Our extension, dubbed NCES2, comes with an improved training data generator and does not require pretrained embeddings for the input knowledge base as both the embedding model and the class expression synthesizer are trained jointly. Empirical results on benchmark datasets suggest that our approach inherits the scalability capability of current NCES instances with the additional advantage that it supports more complex learning problems. NCES2 achieves the highest performance overall when compared to search-based approaches and to its predecessor NCES. We provide our source code, datasets, and pretrained models at https://github.com/dice-group/NCES2.

Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete. Answering queries on such incomplete graphs is an important, but challenging problem. Recently, a number of approaches, including complex query decomposition (CQD), have been proposed to answer complex, multi-hop queries with conjunctions and disjunctions on such graphs. However, these approaches only consider graphs consisting of entities and relations, neglecting literal values. In this paper, we propose LitCQD—an approach to answer complex, multi-hop queries where both the query and the knowledge graph can contain numeric literal values: LitCQD can answer queries having numerical answers or having entity answers satisfying numerical constraints. For example, it allows to query (1) persons living in New York having a certain age, and (2) the average age of persons living in New York. We evaluate LitCQD on query types with and without literal values. To evaluate LitCQD, we generate complex, multi-hop queries and their expected answers on a version of the FB15k-237 dataset that was extended by literal values.

A growing number of knowledge graph embedding models exploit the characteristics of division algebras (e.g., \(\mathbb {R}\), \(\mathbb {C}\), \(\mathbb {H}\), and \(\mathbb {O}\)) to learn embeddings. Yet, recent empirical results suggest that the suitability of algebras is contingent upon the knowledge graph being embedded. In this work, we tackle the challenge of selecting the algebra within which a given knowledge graph should be embedded by exploiting the fact that Clifford algebras \(Cl_{p,q}\) generalize over \(\mathbb {R}\), \(\mathbb {C}\), \(\mathbb {H}\), and \(\mathbb {O}\). Our embedding approach, Keci, is the first knowledge graph embedding model that can parameterize the algebra within which it operates. With Keci, the selection of an underlying algebra becomes a part of the learning process. Specifically, Keci starts the training process by learning real-valued embeddings for entities and relations in \(\mathbb {R}^m=Cl^m_{0,0}\). At each mini-batch update, Keci can steer the training process from \(Cl^m_{p,q}\) to \(Cl^m_{p+1,q}\) or \(Cl^m_{p,q+1}\) by processing the training loss. In this way, Keci can decide the algebra within which it operates in a data-driven fashion. Consequently, Keci is a generalization of previous approaches such as DistMult, ComplEx, QuatE, and OMult. Our evaluation suggests that Keci outperforms state-of-the-art embedding approaches on seven benchmark datasets. We provide an open-source implementation of Keci, including pre-trained models, training and evaluation scripts (https://github.com/dice-group/dice-embeddings).