Document-level Relation Extraction using Reinforcement Learning with Knowledge Graph Feedback - Manzoor, Ali; Zahera, Hammada M.; Saleem, Muhammad; Mahmood, Yasir; Speck, René; Khan, Hashim; Ngonga Ngomo, Axel-Cyrille (The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings)
Keywords: dice hamada hashim kiakadamie mahmood manzoor ngonga rene sail saleem

Von Baumdaten zu Datenwäldern. Eine Umweltgeschichte der Informationsverarbeitung im Ausklang des langen 19. Jahrhunderts - Schmitt, Martin (Rechnen, Zeichnen, Reden Zur Geschichte der Datenverarbeitung im langen 19. Jahrhundert)
Keywords: Umweltgeschichte; 19. Jahrhundert; Deutschland; Forstwirtschaft; Konrad Zuse; Datenverarbeitung; Umweltdaten

Textlinguistik und Textsortenlinguistik. Einführung. - Markewitz, Friedrich
Diese Einführung bietet umfassende Einblicke in die Entwicklung der Text- und Textsortenlinguistik und vermittelt praxisnahes Wissen zur Analyse sowie Klassifikation von Texten. Mit theoretischen Ansätzen und interdisziplinären Perspektiven aus Diskurs- und Systemtheorie sowie den Kulturwissenschaften wird die Bedeutung von Texten wie Textsorten in ihren sozialen, kulturellen und historischen Kontexten beleuchtet. Anhand aktueller Beispiele wie Filmkritiken, Tagebüchern und Memes werden Funktion, Struktur sowie weitere Ausgestaltungsdimensionen von Texten wie Textsorten aufgearbeitet und Studierende der Sprach-, Medien- und Kulturwissenschaften angeleitet, textuelle Phänomene in ihren vielgestaltigen Ausprägungen erfassen zu können.

SIRA: Scaled-Integer Range Analysis for Optimizing FPGA Dataflow Neural Network Accelerators - Umuroglu, Yaman; Berganski, Christoph; Jentzsch, Felix; Danilowicz, Michal; Kryjak, Tomasz; Bezaitis, Charalampos; Sjalander, Magnus; Colbert, Ian; Preusser, Thomas; Petri-Koenig, Jakoba; Blott, Michaela (ACM Transactions on Reconfigurable Technology and Systems)
While neural network quantization effectively reduces the cost of matrix multiplications, aggressive quantization can expose non-matrix-multiply operations as significant performance and resource bottlenecks on embedded systems. Addressing such bottlenecks requires a comprehensive approach to tailoring the precision across operations in the inference computation. To this end, we introduce scaled-integer range analysis (SIRA), a static analysis technique employing interval arithmetic to determine the range, scale, and bias for tensors in quantized neural networks. We show how this information can be exploited to reduce the resource footprint of FPGA dataflow neural network accelerators via tailored bitwidth adaptation for accumulators and downstream operations, aggregation of scales and biases, and conversion of consecutive elementwise operations to thresholding operations. We integrate SIRA-driven optimizations into the open-source FINN framework, then evaluate their effectiveness across a range of quantized neural network workloads and compare implementation alternatives for non-matrix-multiply operations. We demonstrate an average reduction of 17% for LUTs, 66% for DSPs, and 22% for accumulator bitwidths with SIRA optimizations, providing detailed benchmark analysis and analytical models to guide the implementation style for non-matrix layers. Finally, we open-source SIRA to facilitate community exploration of its benefits across various applications and hardware platforms.

Impact of Vehicle Scheduling and Strategic Transition Planning on Zero-Emission Bus Systems - Stumpe, Miriam; Rößler-von Saß, David; Natalia, Kliewer; Schryen, Guido (Transportation Research Interdisciplinary Perspectives)
This paper presents a holistic framework for the transition from diesel to electric bus networks, crucial for meeting EU regulations targeting 100% zero-emission urban buses by 2035. We employ a two-phase solution framework: in phase 1, we solve the Charging Location and Electric Vehicle Scheduling Problem to generate vehicle schedules that are feasible for electric operation; in phase 2, these schedules serve as input to a multi-period transition planning model that minimizes the total cost of ownership while determining fleet replacement and charging infrastructure deployment. Our experiments show that schedules obtained from solving the integrated charging location and vehicle scheduling problem significantly outperform traditional methods, resulting in lower total cost of ownership. Additionally, transition plans reduce local emissions by up to 85% compared to a diesel-only scenario. We find that vehicle rotations with long distances and sufficient idle time are prioritized for electrification, enabling earlier emission reductions and cost savings. This highlights the importance of adopting vehicle scheduling tailored for electric buses, rather than relying on legacy diesel schedules.
Keywords: electric bus; multi-period planning; electric vehicle scheduling; charging infrastructure; fleet replacement

Player-Perceived Workplace Quality and Team Performance: Evidence from NFLPA Report Cards - Protte, Marius
This paper examines whether player-reported workplace quality is associated with team success in the National Football League (NFL). Using panel data for all 32 NFL teams across four seasons (2022-2025), we test whether NFLPA report card rankings-player evaluations of facilities, travel, medical support, coaching, and organizational environment-are related to regular season win percentage. Fixed effects models controlling for player quality, roster composition, injuries, coaching tenure, and past performance reveal a statistically significant within-team association between better player-reported workplace conditions and higher win percentages. However, this relationship does not persist when workplace quality is lagged, suggesting that player evaluations may partly reflect current team performance rather than predict future outcomes. These findings indicate that player evaluations of workplace quality are closely aligned with team success, highlighting the role of perception and short-run performance dynamics in a high-skill labor market setting.
Keywords: NFL team performance; NFLPA report cards; player satisfaction; organizational environment; non-pecuniary compensation

Does inflammation explain the association between vitamin D and depression? Results of a cross-sectional study in children and adolescents - Schlarbaum, Laura; Jankovic, Nicole; Bühlmeier, Judith; Engler, Harald; Hirtz, Raphael; Grasemann, Corinna; Peters, Triinu; Hinney, Anke; Antel, Jochen; Hebebrand, Johannes; Föcker, Manuel; Libuda, Lars (British Journal of Nutrition)
Abstract Vitamin D has been associated with depression, potentially via anti-inflammatory mechanisms, yet data is scarce, particularly in adolescence. We investigated (1) whether lower vitamin D status is associated with greater depression severity and (2) whether this association is statistically moderated by inflammation in patients of a child and adolescent psychiatry department. At admission fasting morning venous blood was drawn. Serum vitamin D (25(OH)D) and C-reactive protein (CRP) were analyzed in all participants [n=465 (64.7%♀; 11.3-18.9 years)]. In a subsample [n=177], we additionally measured tumor necrosis factor-alpha, interferon-gamma and interleukin (IL)-1β, IL-6, IL-8, IL-10. Depression severity was assessed by the Beck Depression Inventory-II (BDI-II) [n=450], the Diagnostic System for Mental Disorders in Childhood and Adolescence via self-assessment (DISYPS Self) [n=441], and parent-assessment (DISYPS Proxy) [n=422]. Overall, 43.2% [n=201] were at risk for vitamin D deficiency (<30nmol/L), and 73.5%-83.2% –depending on assessment tool– showed at least mild depression. Linear regression revealed an inverse association between 25(OH)D and BDI-II in both crude and CRP-adjusted full-sample models. Logistic regressions showed a robust inverse association between 25(OH)D and DISYPS Proxy, but not for DISYPS Self. Although 25(OH)D was inversely correlated with some pro-inflammatory markers, neither their inclusion in regression models nor formal mediation analyses supported inflammation as a mediator of the vitamin D–depression association. Overall, our results suggest that vitamin D relates modestly to both depression and inflammation in adolescence. However, based on the measured parameters, we cannot confirm that anti-inflammatory effects are the link between vitamin D and depression.

Heuristic & Expert-Guided Buffer Sizing for Neural Network Inference Applications on FPGAs - Stasytis, Lukas; Jentzsch, Felix; Preusser, Thomas; Umuroglu, Yaman; Petri-Koenig, Jakoba; István, Zsolt (2025 International Conference on Field Programmable Technology (ICFPT))

Learning science and the illusion of understanding: exploring the effects of integrating learning tasks after explainer videos - Hörnlein, Madeleine; Kulgemeyer, Christoph (International Journal of Science Education)

Empirical QoR Estimation Flow for Fast Design Space Exploration of DNN Dataflow Accelerators - Jentzsch, Felix; Platzner, Marco (2025 International Conference on Field Programmable Technology (ICFPT))

Versprachlichung von Anteilen und natürlichen Häufigkeiten anhand von Baum- und Netzdiagrammen Verbalization of proportions and natural frequencies based on tree diagrams and net diagrams - Rößner, Michael; Binder, Karin; Albrecht, Julian (Journal für Mathematik-Didaktik)
Zusammenfassung Risikokompetenz beinhaltet auch die Fähigkeit, stochastische Informationen, wie beispielsweise Anteile und Wahrscheinlichkeiten, richtig zu versprachlichen. Aus der Forschung zu bedingten Wahrscheinlichkeiten und Bayesianischen Aufgaben ist bekannt, dass die Nutzung von Visualisierungen und sogenannten „natürlichen Häufigkeiten“ (z. B. „80 von 100 Personen“) statt Wahrscheinlichkeiten in Prozent Verwechslungen beim Bestimmen von Wahrscheinlichkeiten eindämmen kann. Über den umgekehrten Prozess – das Versprachlichen von in Visualisierungen dargestellten Informationen – ist bisher jedoch wenig bekannt, obwohl diese Versprachlichungen auch für den Aufbau konzeptuellen Wissen als essentiell angesehen werden. In der vorliegenden Studie wurde daher untersucht, wie gut Schüler:innen die Versprachlichung von in Visualisierungen dargestellten Anteilen und natürlichen Häufigkeiten gelingt. Dazu wurde mit 138 Realschüler:innen aus der 9. Jahrgangsstufe ein Papier-und-Bleistift-Test durchgeführt, bei dem den Teilnehmenden nacheinander je ein (vollständig ausgefülltes) Baumdiagramm und ein Netzdiagramm präsentiert wurde. Die Schüler:innen sollten die inhaltliche Bedeutung der in der Visualisierung dargestellten stochastischen Informationen möglichst genau versprachlichen. Fokus der Studie ist der Einfluss der Visualisierung (Baumdiagramm vs. Netzdiagramm) und des Informationsformats der Visualisierung (Anteile in Prozent vs. natürliche Häufigkeiten) auf die richtige Versprachlichung von verschiedenen Relationstypen (Schnittinformationen vs. bedingte Informationen). Die Ergebnisse zeigen unter anderem, dass Informationen in natürlichen Häufigkeiten deutlich besser versprachlicht werden als in Prozenten und dass Schnittinformationen in Prozent besser anhand von Netzdiagrammen als von Baumdiagrammen versprachlicht werden. Die gewonnenen Erkenntnisse geben überdies Einblicke in typische (fehlerhafte) Versprachlichungen von Schüler:innen und könnten als Basis für die Entwicklung eines sprachsensiblen Unterrichts zu Anteilen und Wahrscheinlichkeiten im schulischen Stochastikunterricht dienen.

ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application - Malena, Kevin; Link, Christopher; Gausemeier, Sandra; Trächtler, Ansgar (2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC))
This paper deals with the development and results of a prediction framework for traffic light control systems as well as the usage and benefits of such predictions in green light optimal speed advisory (GLOSA) scenarios. Various machine learning methods like support vector machines, neural networks or reinforcement learning were evaluated for their applicability in the prediction context and compared based on their efficiency and most importantly accuracy. The resulting prediction framework uses decision tree ensemble models combined with certain model knowledge to forecast different control strategies. This method was chosen due to its best performance in various test scenarios. Very high accuracy and fidelity were achieved for standard control methods like fixed-time, time-of-day-based and 'ordinary' traffic-based programs. Only for the more sophisticated model predictive control which was tested lower accuracies were achieved. For the upcoming GLOSA application the penetration of equipped vehicles was varied for different traffic scenarios and control strategies. Results showcase high potentials for enhancing urban mobility and reducing environmental impact by lower emissions and waiting times. However, it is also clear from the studies presented in this contribution that the coordination of the control strategy with the GLOSA vehicles is of enormous importance.
Keywords: ML; Prediction; Tree Ensembles; GLOSA

Response surface methodology for parameter development of alloy UNS N09946 processed with laser powder bed fusion - Wooldridge, Madison; Holzweissig, Martin; Hoyer, Kay-Peter; Schaper, Mirko (Rapid Prototyping Journal)
Purpose The adoption of laser powder bed fusion (LPBF) as an additive manufacturing technique has been slow in the oil and gas (O&G) industry because of the uncertainty regarding material performance and the lack of suitable materials. The high investment and time required for LPBF development also discourage adoption. This study aims to address these concerns by developing a parameter set for a relevant material using a systematic approach to optimize the density of the printed parts with reduced experimental effort. Design/methodology/approach First, an industry-relevant Ni-based superalloy, UNS N09946, was gas-atomized to produce a powder. The powder was fully characterized to ensure successful printing. Next, a processing parameter set tailored for achieving full density was developed for UNS N09946 using a Design of Experiments (DoE) approach based on the volumetric energy density equation. Findings A model was created using Response Surface Methodology that relates laser power, scan speed and hatch distance to efficiently identify successful parameter combinations, thus reducing the number of specimens necessary for the successful manufacturing of UNS N09946 using LPBF. A part density of 99.9% was achieved using this method. Originality/value This study applies an existing experimental design method to a never-before-printed material. The reduced experimental effort through this method and lessons learned from the gas atomization process can be directly applied to other materials in and outside the O&G industry to further the adoption of LPBF as a serious manufacturing technology.

Introduction - Dockter, Cornelia; Lebock, Sarah; Wiesenhütter, Lukas (Religion and Health - Comparative-theological Approaches)

Designing and Evaluating the Usability and Onboarding for a Mixed-Reality Application: A Case Study with PEARL (Paderborn Electrical Engineering AR Laboratory) and Meta Quest 3 - Alptekin, Mesut; Münstermann, Daniel; Temmen, Katrin (Lecture Notes in Networks and Systems)
Abstract This study explores the usability and onboarding process of a Mixed Reality (MR) application called PEARL, designed to prepare students for laboratory work. Originally developed for mobile Augmented Reality (mAR), PEARL was adapted for MR to offer a more immersive and intuitive experience through hand and gesture controls. Since many students lack experience with MR devices, a user-friendly onboarding system is essential. The study aims to redesign PEARL’s user interface and onboarding experience, evaluating how intuitive interaction elements impact usability. First, a literature review will identify existing usability guidelines for MR applications, which will guide the redesign of the interface. This new version will then be tested with students through a user study. Feedback will be collected via an online survey to assess the onboarding and user experience, and the findings will be used to refine the design further. The expected outcome is an improved onboarding process and interface, making PEARL accessible even for MR novices, enhancing their ability to interact with 3D objects in a real-world setting. Ultimately, the study aims to provide best practices for developing intuitive MR interfaces and effective onboarding experiences, especially in educational contexts.
Keywords: Meta Quest 3; Augmented Reality; Mixed Reality; PEARL; Electrical Engineering; Laboratory Training; Onboarding; User Experience; User Interface; Heuristics

dbs Summer School Klinische Linguistik 2024. Sprachtherapie bei Kindern und Jugendlichen mit Schädel-Hirn-Trauma - Rosenkranz, A.; Kuhles, G.; Falkowska, Z.; Jaecks, P.; von Lehmden, F.; Büttner-Kunert, J.; Jonas, Kristina (Sprachtherapie aktuell: Forschung – Wissen – Transfer 13(1). dbs Summer School Klinische Linguistik)

Precise Optical Nyquist Pulse Synthesizer Digital-to-Analog Converter - Scheytt, J. Christoph; Schwabe, Tobias; Singh, Karanveer; Kress, Christian; Schneider, Thomas (Electronic-Photonic Integrated Systems for Ultrafast Signal Processing)
Abstract Optically assisted digital-to-analog converters (DACs) using Nyquist pulse sequences (NPSs) are presented and investigated. Therefore, NPSs are mathematically described and analyzed. Based on this, the operating principle of a precise optical Nyquist pulse synthesizer digital-to-analog converter (PONyDAC) is described. Possible architectures of PONyDAC are derived and compared in terms of performance and practicability. Moreover, the limits of PONyDAC systems and their superiority over classical electronic DACs are discussed. Furthermore, discrete building-block based implementations and monolithic implementations in electronic-photonic integrated circuits (EPICs) are presented. To enable a practicable monolithic integration, a shrinkage of the Mach-Zehnder modulators (MZMs) has been performed by applying forward-biased phase shifters (FB-PSs). These FB-PSs are analyzed and modeled to allow the precise and reliable design of PONyDAC systems with multiple MZMs. Finally, data conversion and data transmission experiments are carried out to demonstrate the systems functionality, quantify its performance, and prove their superiority over purely electronic DACs.

Welterfahrungsspiele und chronotopische Visiten. Reisethemen in Gedichten von Jan Wagner und Nora Bossong - Elit, Stefan (Erzählen zwischen gestern und morgen: Nora Bossong. Paderborn, Wintersemester 2024/25)

Erzählen zwischen gestern und morgen. Nora Bossong, Paderborn Wintersemester 2024/25: 43. Paderborner Gastdozentur für Schriftstellerinnen und Schriftsteller

Electronic-Photonic Integrated Systems for Ultrafast Signal Processing