@inproceedings{7175,
  abstract     = {Deep reinforcement learning (RL) policies for robotic control are typically trained for a single hardware configuration. When a joint locks, the learned mapping no longer matches the available actuation. This paper investigates whether combining self-attention with explicit kinematic observations can improve a policy’s adaptability to varying active-joint configurations. We train a single RL agent to control a Franka Emika Panda manipulator across different sets of active joints, from a restricted 4-degree-of-freedom (DOF) mode to the fully redundant 7-DOF configuration. Instead of fixed-length state vectors, the method processes the active joints as a variable-length sequence, enriching each joint’s representation with its relative spatial offset and geometric Jacobian influence. The proposed kinematics-informed attention architecture reaches a success rate of 82.1% across trained configurations and 66.6% on unseen configurations, outperforming multilayer perceptron and generic attention baselines while remaining nearly collision-free. Deployed on a physical Panda, the simulation-trained agent reaches 79.0% success on the unseen configurations, closely matching the simulation on the same target schedule (77.0%).},
  author       = {Mayer, Patrick Thomas and Rexilius, Jan},
  booktitle    = {9th Iberian Robotics Conference (ROBOT) },
  keywords     = {Reinforcement Learning, Robotic Manipulation, Kinematic Adaptation, Sim-to-Real Transfer},
  location     = {Barcelona},
  title        = {{Adapting to Variable Active-Joint Configurations via Kinematics-Informed Attention for Robot Arms}},
  year         = {2026},
}

@inproceedings{7174,
  abstract     = {This paper presents a hybrid control architecture for dynamic robotic picking tasks. The framework combines a Deep Reinforcement Learning policy for high-level interception with a dedicated Inverse Kinematics controller for precise terminal grasping, while mitigating precision limitations of monolithic learning-based approaches. The framework utilizes a Proximal Policy Optimization agent to approach moving targets, seamlessly transitioning to an Inverse Kinematics solver that reduces terminal orientational and positional errors while minimizing cumulative control effort. To facilitate deployment on physical hardware, a robust sim-to-real pipeline incorporating system identification, domain randomization, and latency injection is employed. Experimental results on a physical Franka Emika Panda manipulator validate this hybrid architecture. The system achieves an 80% success rate in pick-and-place tasks, compared to 60.8% for unadapted baselines, with no safety-critical violations such as joint limit breaches or collisions observed during testing.},
  author       = {Mayer, Patrick Thomas and Rexilius, Jan},
  booktitle    = {26th International Conference on Control, Automation and Systems (ICCAS)},
  keywords     = {Reinforcement Learning, Hybrid Control, Sim-to-Real, Robotic Manipulation, Inverse Kinematics.},
  location     = {Sapporo},
  title        = {{A Hybrid Control Framework Using Reinforcement Learning for Dynamic Robotic Manipulation and Sim-to-Real Transfer}},
  year         = {2026},
}

@inproceedings{7173,
  abstract     = {Reinforcement learning has achieved state-of-the-art performance in MAV control, waypoint flight, and obstacle avoidance. However, existing RL approaches often assume fixed objectives and constraints, with flight behavior largely limited by vehicle dynamics and orientation considered only when required for locomotion. Classical planning and model predictive control handle such constraints explicitly, but require optimization or replanning. This motivates methods that combine learned local control with explicit constraint handling. We combine reinforcement learning with control barrier functions to improve constraint-aware execution. We propose parameterized waypoints that encode orientation, velocity, and corridor constraints. Simulation and real-world experiments show that a single policy can execute different constraint-parameterized navigation scenarios, revealing scenario-dependent trade-offs between traversal time, tracking accuracy, and constraint satisfaction.},
  author       = {Kirsch, André and Rexilius, Jan},
  keywords     = {MAV navigation, Reinforcement learning, Constrained navigation, Control barrier functions},
  location     = {Barcelona},
  title        = {{Reinforcement Learning-based MAV Navigation With Parameterized Waypoints: Incorporating Orientation, Speed Limits, and Corridor Constraints}},
  year         = {2026},
}

@inproceedings{7068,
  abstract     = {Adopting digital twin technologies in small andmedium-sized enterprises (SMEs) is often hinderedby heterogeneous, poorly documented productiondata. Current semantic type detection approachesrequire massive labeled datasets making themimpractical for resource-constrained SMEs. Wepropose a zero-shot hybrid framework combiningpattern-based classification with selective largelanguage model (LLM) reasoning formanufacturing-specific data types. The two-stagearchitecture uses rule-based patterns for high-confidence cases, forwarding ambiguous columnsto a multi-step LLM reasoner. Evaluation on fourmanufacturing datasets shows the hybrid approachachieves weighted F1 within 7 24 percentagepoints of pure LLM classification performancewhile reducing LLM invocations by 39% onaverage. Processing time decreased by up to 2.8×.Our framework addresses a critical gap: automated,computationally efficient data type recognition formanufacturing SMEs without requiring trainingdata, contributing to automated simulation anddigital twin construction.},
  author       = {Döring, Lina and Trojahn, Sebastian and Reusch, Pascal},
  booktitle    = {19th International Doctoral Students Workshop on Logistics, Supply Chain and Production Management},
  editor       = {Behrendt, Fabian and Zadek, Hartmut and Janmontree, Jettarat and Trojahn, Sebastian and Lang, Sebastian},
  keywords     = {Semantic Data Type Detection, DigitalTwin, Manufacturing SMEs, Zero-shot Learning, Hybrid AI, LLM},
  location     = {Magdeburg},
  publisher    = {Otto von Guericke University Library, Magdeburg, Germany},
  title        = {{Smart data adapter: A hybrid pattern-LLM approach}},
  doi          = {10.25673/123544},
  year         = {2026},
}

@article{7034,
  author       = {Prell, Jonas and Schmidtmann, Achim},
  issn         = {1860-6725},
  journal      = {ERP Management},
  keywords     = {ERP-Management, Adaptives SLA, IT-Governance, IT-Outsourcing, Innovationsmanagement},
  number       = {3},
  pages        = {28--33},
  publisher    = {GITO Verlag},
  title        = {{Adaptive Service Level Agreements}},
  volume       = {22},
  year         = {2026},
}

@techreport{6827,
  abstract     = {Introduction: Migration is a complicated social phenomenon that has a big impact on people's access to and use of health care, especially for vulnerable groups like women and children. The study was carried out to determine the factors affecting the potential use of health services and their usage by female Turkish migrants and their children living in Bielefeld, Germany.
Methods: The study adopted a qualitative design, and in-depth individual interview interviews were used to collect data. Interviews were conducted with 20 female Turkish migrants who have children with food allergies between November-December 2023. The data were analyzed using the content analysis method. 
Results: The ages of the female Turkish migrants participating in the study ranged from 31 to 48. A total of 4 themes were determined from the codes: 1) Perceptions on health services, 2) Perceptions of health care service in Germany compared to the country of origin, 3) Healthcare utilization and comparative perceptions of health systems, and 4) Factors that facilitate access to health services and increase satisfaction and suggestions for improvement. 
Discussion: Mothers have both positive and negative experiences with the German healthcare system, their perceptions of the German healthcare system are influenced by their experiences in their home country, there are factors that increase their satisfaction, they encounter individual and systemic barriers in healthcare service utilization, and they made some suggestions for overcoming these barriers.
},
  author       = {Makowsky, Katja and Sezer Efe, Yağmur },
  keywords     = {German healthcare system, factors affecting the potential use of health services, female Turkish migrants, their children},
  title        = {{UTILIZATION AND USE OF HEALTH SERVICES BY FEMALE TURKISH MIGRANTS AND THEIR CHILDREN LIVING IN GERMANY}},
  doi          = {10.57720/6827},
  year         = {2026},
}

@article{6244,
  author       = {Niederhaus, Marvin and Migenda, Nico and Weller, Julian and Kohlhase, Martin and Schenck, Wolfram},
  issn         = {2504-2289},
  journal      = {Big Data and Cognitive Computing},
  keywords     = {prescriptive analytics, prescriptive platforms, advanced data analytics, retrieval-augmented generation, graph-based retrieval-augmented generation, large language models, generative AI, genAI, recommender system},
  number       = {10},
  publisher    = {MDPI AG},
  title        = {{Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems}},
  doi          = {10.3390/bdcc9100261},
  volume       = {9},
  year         = {2025},
}

@inproceedings{6235,
  author       = {Sangel, Marius and Bensch, Emilia and Brandt-Pook, Hans and Röllke, Timo and Markworth, Cedric},
  booktitle    = {INFORMATIK 2025},
  keywords     = {Machine Learning, Computer Vision, Instance Segmentation, CNNs, YOLACT, Data Augmentation, Waste Classification, Trash Detection, Biowaste Analysis},
  location     = {Potsdam},
  number       = {366},
  pages        = {1363--1371},
  title        = {{Automatisierte Erkennung von Störstoffen in Bioabfall mit maschinellem Lernen: Ansätze und Ergebnisse aus dem Projekt TRACES}},
  doi          = {10.18420/INF2025_121},
  year         = {2025},
}

@inproceedings{5905,
  author       = {Jaster, Bjarne and Kohlhase, Martin},
  booktitle    = {2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion)},
  keywords     = {Active Learning, Trustworthiness, Evaluation-methods},
  location     = {Trondheim, Norway},
  pages        = {1--5},
  publisher    = {IEEE},
  title        = {{Trust Issues in Active Learning and Their Impact on Real-World Applications}},
  doi          = {10.1109/CITRExCompanion65208.2025.10981492},
  year         = {2025},
}

@inproceedings{6045,
  author       = {Jaster, Bjarne and Kohlhase, Martin},
  booktitle    = {2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx Companion)},
  keywords     = {Active Learning, Trustworthiness, Evaluation-methods},
  location     = {Trondheim, Norway},
  pages        = {1--5},
  publisher    = {IEEE},
  title        = {{Trust Issues in Active Learning and Their Impact on Real-World Applications}},
  doi          = {10.57720/6045},
  year         = {2025},
}

@inproceedings{6049,
  author       = {Schöne, Marvin and Jaster, Bjarne and Bültemeier, Julian and Kösters, Justus and Holst, Christoph-Alexander and Kohlhase, Martin},
  booktitle    = {2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx)},
  keywords     = {Interpretability, Classification, Pool-based Active Learning, Decision Trees},
  location     = {Trondheim, Norway},
  pages        = {1--9},
  publisher    = {IEEE},
  title        = {{Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit}},
  doi          = {10.57720/6049},
  year         = {2025},
}

@inproceedings{5904,
  author       = {Schöne, Marvin and Jaster, Bjarne and Bültemeier, Julian and Kösters, Justus and Holst, Christoph-Alexander and Kohlhase, Martin},
  booktitle    = {2025 IEEE Symposium on Trustworthy, Explainable and Responsible Computational Intelligence (CITREx)},
  keywords     = {Interpretability, Classification, Pool-based Active Learning, Decision Trees},
  location     = {Trondheim, Norway},
  pages        = {1--9},
  publisher    = {IEEE},
  title        = {{Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit}},
  doi          = {10.1109/CITREx64975.2025.10974940},
  year         = {2025},
}

@article{4278,
  author       = {Schnatmann, Anna Katharina and Reimers, Tobi and Hüdepohl, Erik and Umlauf, Jonah and Kleinebekel, Pia and Schoden, Fabian and Schwenzfeier-Hellkamp, Eva},
  issn         = {2071-1050},
  journal      = {Sustainability},
  keywords     = {circular economy, crystalline photovoltaics, degradation, quality management, reuse, second-life PV, electroluminescence},
  number       = {3},
  publisher    = {MDPI AG},
  title        = {{Investigating the Technical Reuse Potential of Crystalline Photovoltaic Modules with Regard to a Recycling Alternative}},
  doi          = {10.3390/su16030958},
  volume       = {16},
  year         = {2024},
}

@phdthesis{4392,
  author       = {Grumbach, Felix},
  keywords     = {Produktionsplanung und -steuerung, Operations Research, Machine Learning, Reinforcement Learning},
  publisher    = {Universitäts- und Landesbibliothek Sachsen-Anhalt},
  title        = {{Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse mit Techniken des maschinellen Lernens [kumulative Dissertation]}},
  doi          = {10.25673/115290},
  year         = {2024},
}

@article{5022,
  author       = {Davies, Emma and Schwenzfeier-Hellkamp, Eva and Ehrmann, Andrea},
  issn         = {2227-9717},
  journal      = {Processes},
  keywords     = {hydrogen storage system, hydrogen transport, hydrogen use, leakage, explosion, pressure},
  number       = {10},
  publisher    = {MDPI AG},
  title        = {{Safety of Hydrogen Storage Technologies}},
  doi          = {10.3390/pr12102182},
  volume       = {12},
  year         = {2024},
}

@book{4998,
  author       = {Köhler, Gerhard and Roth, Werner and Schmidtmann, Achim},
  isbn         = {978-3-658-45535-4},
  keywords     = {IT-Outsourcing, IT, IT-Service, Ausschreibung, Agilität, IT-Dienstleistung, IT-Service-Management, Agile, Ausschreibungsprozess},
  pages        = {259},
  publisher    = {Springer Fachmedien Wiesbaden},
  title        = {{Adaptive IT-Service-Ausschreibung. Der Weg zu agilem und effektiverem IT-(Out)Sourcing}},
  doi          = {10.1007/978-3-658-45536-1},
  year         = {2024},
}

@article{4881,
  author       = {Wiegraebe, Frauke and Schönbeck, Marleen and Wunderlich, Paul and Nauerth, Annette and Dörksen, Helene},
  issn         = {1430-9653},
  journal      = {Pflege und Gesellschaft},
  keywords     = {Digital care support tool, caring workforce, interdisciplinarity, AI application, machine learning, user orientation},
  pages        = {271--285},
  publisher    = {BeltzJuventa},
  title        = {{KI-basiertes Unterstützungstool für pflegende Erwerbstätige}},
  doi          = {10.3262/P&G2403271},
  volume       = {3},
  year         = {2024},
}

@inproceedings{4394,
  author       = {Müller, Anna and Grumbach, Felix},
  booktitle    = {16th International Doctoral Students Workshop on Logistics, Supply Chain and Production Management},
  editor       = {Glistau, Elke and Trojahn, Sebastian},
  keywords     = {Machine Learning, Production scheduling, time prediction},
  location     = {Magdeburg},
  publisher    = {Otto von Guericke University Library, Magdeburg, Germany},
  title        = {{Predicting processing times in high mix low volume job shops}},
  doi          = {10.25673/103491},
  year         = {2023},
}

@article{2830,
  author       = {Klemme, Isabel and Wrona, Kamil Joseph and de Jong, Irja Marije and Dockweiler, Christoph and Aschentrup, Leona and Albrecht, Joanna},
  issn         = {2371-4379},
  journal      = {JMIR Diabetes},
  keywords     = {people with type 1 diabetes, self-management, diabetes apps, vision assessment, anticipated stigma, qualitative research, Digitale Gesundheitsanwendungen, DiGA, mobile phone},
  publisher    = {JMIR Publications Inc.},
  title        = {{Integration of the Vision of People With Diabetes Into the Development Process to Improve Self-management via Diabetes Apps: Qualitative Interview Study}},
  doi          = {10.2196/38474},
  volume       = {8},
  year         = {2023},
}

@techreport{3729,
  author       = {Kösters, Justus and Schöne, Marvin and Kohlhase, Martin},
  keywords     = {tabular scarce data, industrial design, supervised machine learning models},
  title        = {{Benchmarking of Machine Learning Models for Tabular Scarce Data}},
  year         = {2023},
}