@book{54259,
  editor       = {{Janus, Richard}},
  pages        = {{16}},
  title        = {{{Reformation mit Dorothee Sölle feiern. 31. Oktober 2023}}},
  volume       = {{4}},
  year         = {{2023}},
}

@misc{54274,
  author       = {{Janus, Richard}},
  pages        = {{1}},
  publisher    = {{Evangelischer Bund}},
  title        = {{{Mahlzeit! Essen ist mehr als Nahrungsaufnahme}}},
  volume       = {{2023/4}},
  year         = {{2023}},
}

@inproceedings{54286,
  abstract     = {{<jats:p>The integration of Artificial Intelligence (AI) techniques into various domains has revolutionized numerous industries, and Supply Chain Management (SCM) is no exception. This paper addresses the challenges encountered in SCM and the development of AI solutions within this context. Specifically, we focus on the application of AI in optimizing supply chain planning tasks. This includes forecasting demand, availability and feasibility checks for customer orders, supply chain network design and information flow inside the supply chain planning processes.  However, the successful implementation of AI in SCM requires a deep understanding of both the domain-specific challenges and the capabilities and limitations of AI technologies. Thus, this paper proposes an overarching approach that facilitates collaboration between domain experts in SCM and AI experts, enabling them to jointly develop effective solutions.The paper begins by outlining the key challenges faced by SCM professionals, including demand volatility, complexities in inventory management, and dynamic market conditions. Subsequently, it delves into the challenges associated with developing AI solutions for SCM, including data quality, interpretability, and model transparency. To address these challenges, the proposed approach promotes close collaboration and knowledge exchange between SCM and AI experts. By leveraging the domain knowledge and experience of SCM experts, AI experts  can better understand the special issues of SCM processes and tailor AI techniques to suit specific needs. In turn, SCM experts can gain insights into the capabilities and limitations of AI, allowing them to make informed decisions regarding the adoption and integration of AI in their supply chain planning operations. Furthermore, the paper discusses the importance of establishing a multidisciplinary team comprising experts from the fields of SCM, AI, and IT.   This team-based approach fosters a holistic understanding of SCM challenges and ensures the development of AI solutions that align with business goals and practical constraints.In conclusion, this paper highlights the challenges in combining SCM and AI and proposes a collaborative approach to address these challenges effectively. By leveraging the expertise of both domain and AI experts, organizations can develop tailored AI solutions that enhance supply chain planning, improve decision-making processes, and drive competitive advantage. The proposed approach contributes to the successful integration of AI in SCM, ultimately leading to more efficient and resilient supply chains in the era of artificial intelligence.</jats:p>}},
  author       = {{Lick, Jonas and Wohlers, Benedict and Sahrhage, Philipp and Schreckenberg, Felix and Klöckner, Susanne and Von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{Artificial Intelligence, Social Computing and Wearable Technologies}},
  issn         = {{2771-0718}},
  publisher    = {{AHFE International}},
  title        = {{{Integrating Domain Expertise and Artificial Intelligence for Effective Supply Chain Management Planning Tasks: A Collaborative Approach}}},
  doi          = {{10.54941/ahfe1004185}},
  year         = {{2023}},
}

@article{54282,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Stretching of polycarbonate films leads to the formation of shear bands in the necking zone [1]. Standard viscoplastic material models render mesh size dependent results, which requires a mathematical regularization. To this end, we present a finite strain gradient theory for a viscoplastic, isotropic material model where we extend the model presented in [2] to a micromorphic model by introducing a new micromorphic variable as an additional degree of freedom with its first gradient [3, 4]. The variable here has the meaning of a micro plastic strain, and is coupled with the macro plastic by a micro penalty term, forcing the macro‐plastic strain to be close to the micro‐plastic strain for the targeted shear band regularization effect. We have implemented the model equations as a three dimensional initial boundary value problem in an in house FE‐tool, to simulate different geometries with different thickness and to compare it the experimental tests. The analysis is performed for a uniaxial tensile geometry as well as for a biaxial tensile geometry. The numerical examples show the ability of the model to regularize the shear bands and solve the problem of localization.</jats:p>}},
  author       = {{Hamdoun, Ayoub and Mahnken, Rolf}},
  issn         = {{1617-7061}},
  journal      = {{PAMM}},
  number       = {{1}},
  publisher    = {{Wiley}},
  title        = {{{A finite strain gradient theory for viscoplasticity by means of micromorphic regularization}}},
  doi          = {{10.1002/pamm.202200074}},
  volume       = {{22}},
  year         = {{2023}},
}

@misc{42839,
  author       = {{Mehlich, Florian}},
  publisher    = {{Paderborn University}},
  title        = {{{An Evaluation of XCS on the OpenAI Gym}}},
  year         = {{2023}},
}

@misc{53930,
  author       = {{Tadakamalla, Yashwanth }},
  publisher    = {{Paderborn University}},
  title        = {{{A Comparison of Algorithms for the Generation of Layouts based on Reconfigurable Slots on FPGAs}}},
  year         = {{2023}},
}

@misc{54295,
  author       = {{Middeke, Sorel Horst}},
  publisher    = {{Paderborn University}},
  title        = {{{Design and Realization of Optimized Intra-FPGA ROS 2 Communication}}},
  year         = {{2023}},
}

@misc{54294,
  author       = {{Thiele, Simon}},
  publisher    = {{Paderborn University}},
  title        = {{{A Hardware/Software Co-designed ORB-SLAM2 Algorithm for FPGA}}},
  year         = {{2023}},
}

@misc{54297,
  author       = {{Abooof, Alhussain}},
  publisher    = {{Paderborn University}},
  title        = {{{Implementation and Evaluation of a ReconROS-based Obstacle Avoidance Architecture for Autonomous Robots}}},
  year         = {{2023}},
}

@misc{54296,
  author       = {{Rao, Aniruddh P}},
  publisher    = {{Paderborn University}},
  title        = {{{Multithreaded Software/Hardware Programming with ReconOS/Zephyr on a RISC-V-based System-on-Chip}}},
  year         = {{2023}},
}

@misc{45762,
  author       = {{Simon-Mertens, Florian}},
  publisher    = {{Paderborn University}},
  title        = {{{Effizienzanalyse leichtgewichtiger Neuronaler Netze für FPGA-basierte Modulationsklassifikation}}},
  year         = {{2023}},
}

@misc{54243,
  author       = {{Oviasogie, Marvin Osaretin}},
  publisher    = {{Paderborn University}},
  title        = {{{Demonstrator for Dataflow-based DNN Acceleration for Vision Applications on Platform FPGAs}}},
  year         = {{2023}},
}

@misc{54241,
  author       = {{Reuter, Lucas David}},
  publisher    = {{Paderborn University}},
  title        = {{{Development of a Power Analysis Framework for Embedded FPGA Accelerators}}},
  year         = {{2023}},
}

@misc{54246,
  author       = {{Hamm, Robin}},
  publisher    = {{Paderborn University}},
  title        = {{{Verarbeitung von Sensordaten auf eingebetteten heterogenen FPGA-Systemen}}},
  year         = {{2023}},
}

@misc{52480,
  author       = {{Klassen, Alexander}},
  publisher    = {{Paderborn University}},
  title        = {{{Fast Partial Reconfiguration for ReconOS64 on Xilinx MPSoC Devices}}},
  year         = {{2023}},
}

@misc{54298,
  author       = {{Tsague Dingo, Jorian}},
  publisher    = {{Paderborn University}},
  title        = {{{Ein Simulator für Schedulability-Experimente mit periodischen Tasks auf FPGAs}}},
  year         = {{2023}},
}

@misc{54299,
  author       = {{Brede, Mathis}},
  publisher    = {{Paderborn University}},
  title        = {{{Evaluation of Classifier Migration Between Multiple XCS Populations}}},
  year         = {{2023}},
}

@misc{54300,
  author       = {{Nowosad, Alexander}},
  publisher    = {{Paderborn University}},
  title        = {{{Design and Realization of an Intra-FPGA ROS 2 Communication Infrastructure for the ReconROS Executor}}},
  year         = {{2023}},
}

@misc{54244,
  author       = {{AlAidroos, Salem}},
  publisher    = {{Paderborn University}},
  title        = {{{Design and Implementation of a RadioML Demonstrator based on an RFSoC Platform}}},
  year         = {{2023}},
}

@misc{54242,
  author       = {{Evers, Gerrit}},
  publisher    = {{Paderborn University}},
  title        = {{{Bewertung der Xilinx Runtime Library zur Hardware/Software-Kommunikation}}},
  year         = {{2023}},
}