Refine
Year of publication
Document Type
- Conference Proceeding (1547)
- Article (reviewed) (822)
- Bachelor Thesis (769)
- Article (unreviewed) (701)
- Part of a Book (516)
- Contribution to a Periodical (397)
- Master's Thesis (279)
- Book (261)
- Patent (165)
- Legal Comment (145)
Conference Type
- Konferenzartikel (1172)
- Konferenz-Abstract (197)
- Konferenzband (88)
- Konferenz-Poster (46)
- Sonstiges (46)
Language
- German (3539)
- English (2415)
- Other language (16)
- Multiple languages (7)
- Russian (6)
- Spanish (4)
- French (1)
Keywords
- Künstliche Intelligenz (70)
- Mikroelektronik (70)
- Marketing (57)
- Digitalisierung (54)
- Social Media (52)
- Biomechanik (45)
- COVID-19 (40)
- IT-Sicherheit (37)
- E-Learning (36)
- RoboCup (35)
Institute
- Fakultät Maschinenbau und Verfahrenstechnik (M+V) (1344)
- Fakultät Medien und Informationswesen (M+I) (bis 21.04.2021) (1079)
- Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019) (928)
- Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) (784)
- Fakultät Wirtschaft (W) (753)
- Fakultät Medien (M) (ab 22.04.2021) (604)
- Veröffentlichungen außerhalb der Hochschulzeit (322)
- Zentrale Einrichtungen (84)
- Rektorat/Verwaltung (34)
Open Access
- Open Access (2234)
- Closed Access (2080)
- Closed (1415)
- Bronze (614)
- Diamond (162)
- Gold (134)
- Hybrid (94)
- Grün (26)
Biosurfactants are becoming increasingly popular, but industrial production of biosurfactants is difficult, partly due to high production costs resulting from the need to use expensive substrates. One economically feasible candidate is vegetable oils, which can be directly metabolized without pretreatment. The aim of this work is therefore to investigate the possibility of using vegetable oils for lipopeptide production by Serratia marcescens G8-1. The genetic background of this strain for the production of lipopeptides was investigated using a genomic approach. The biosurfactants were analysed by Ultra-Performance Liquid Chromatography coupled with Electrospray Ionisation Mass Spectrometry. The ability to reduce surface tension was investigated using a tensiometer. The results showed that the best effect in reducing surface tension was achieved by adding waste rapeseed oil. Sunflower and linseed oil also showed good results. Significantly poorer results were obtained when fresh rapeseed oil, sesame oil and pumpkin seed oil were used. The putative gene cluster for cyclic lipopeptides NRPS was identified in the genome of S. marcescens G8-1. The results obtained confirmed that serrawettin W1 is the major biosurfactant produced by S. marcescens G8-1. Of particular interest, the results showed the presence of vinylamycin when rapeseed oil was used.
We propose the extension for Artificial Intelligence (AI)-supported learning recommendations within higher education, focusing on enhancing the widely-used Moodle Learning Management System (LMS) and extending it to the Learning eXperience Platform (LXP). The proposed LXP is an enhancement of Moodle, with an emphasis on learning support and learner motivation, incorporating various recommendation types such as content-based, collaborative, and session-based recommendations to provide the next learning resources given by lecturers and retrieved from the content curation of Open Educational Resources (OER) for the learners. In addition, we integrated a chatbot using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with AI-based recommendations to provide an effective learning experience.
Over the past decade, the popularity of cobots (collaborative robots) has grown, largely due to their operator-friendly usage. When selecting a cobot or robot for a specific application, it is essential to consider which model best aligns with the desired process. The objective of this work is to introduce a method for evaluating the three-dimensional position performance of a given process to identify the optimal technical solution.
This paper presents an empirical study on the influence of network and training settings in data-driven black-box modelling of synchronous motors using neural state-space models. The objective is to provide a foundational insight into neural state-space modelling and reference points for researchers involved in this field. The study examines correlations between hyperparameters in terms of design and training parameters and the resulting model performance, which are difficult to express analytically. Additionally, an in-depth analysis of training times is conducted to identify configurations that achieve comparable model accuracy with reduced computational time. These findings aim to facilitate efficient model development by offering a systematic approach to parameter selection and optimization in black-box modelling for electrical motor applications.
This paper introduces a novel method for the current control of electric motors, enhancing the established field-oriented control used in automotive applications by incorporating angle dependency. This advancement facilitates improved operational capabilities, eliminating the need for multiple existing approaches for tasks such as harmonic suppression, specific harmonic injection, and the generation of additional insights into the motor harmonic behavior. Notably, this method requires only the parameters and sensors typically utilized in conventional current control, ensuring low computational demands. The paper provides a comprehensive explanation of the theoretical framework and implementation of the proposed method. Furthermore, the algorithm is validated through experimental measurements, and the potential future applications, along with inherent limitations, are thoroughly discussed.
Bin-picking systems face challenges in fully emptying containers due to Collision risks in real-world scenarios. In this contribution, we present a grasp planning approach that combines 2D and 3D sensor data to ensure collision-free handling. The method is successfully tested on objects with eccentric centers of mass and is implemented directly on the robot’s control unit, eliminating the need for separate computing hardware.
Additive manufacturing has increasingly found its way into business practice and is a relevant subject of research. In this context, AM using pellets or granulates is a relatively new process that still needs to be explored. This process is sustainable because it supports the elimination of energy-intensive process steps such as filament production. It also opens up new design possibilities because it can process much larger amounts of material per time than many conventional additive manufacturing processes. The aim of this paper is to compare this Fused Granulate Fabrication process with the filament-based Fused Filament Fabrication process and to discuss potential applications on the basis of specific advantages and disadvantages. Both filament-based and pellet-based additive processes are compared and evaluated on the basis of technical criteria, sustainability and cost. The evaluation has shown that pellet-based processes have some advantages in terms of sustainability and material selection. These advantages are offset by disadvantages in terms of processing. This analysis therefore provides a basis for the selection of a suitable process based on various criteria.
Peptidyl-lys metalloendopeptidases (PKMs) are enzymes that selectively cleave peptide bonds at the N-terminus of lysine residues present in the P1′ position, making them valuable tools in proteomics. This mini-review presents an overview of PKMs, covering their traditional production from native sources, recent advances in recombinant production, and the current limitations in availability. The historical and current applications of PKMs in proteomics are discussed, highlighting their role in protein sequencing, peptide mapping, and mass spectrometry-based studies. Advances in recombinant technology now enable tailored modifications to PKM, allowing it to function not only as a sister enzyme to LysC but also to trypsin, thereby enhancing its suitability for specific analytical applications. The mini-review concludes with a forward-looking statement on PKM research, emphasizing the potential to broaden its use in novel proteomic methods and other applications.
In an era of accelerating digital transformation and increasing regulatory scrutiny, third-party risk management (TPRM) has become a strategic imperative for financial institutions. This thesis examines the TPRM framework of Deutsche Börse Group (DBG), a critical financial market infrastructure provider, with the aim of evaluating its current maturity, identifying internal control gaps, and proposing targeted improvements aligned with international regulatory expectations. The research adopts a qualitative case study methodology, leveraging internal documents,stakeholder feedback, and benchmarking against established standards such as DORA, the ECB SSM guidelines, ISO 27001, and COBIT. A detailed internal insight analysis reveals gaps in areas such as automation, fourth-party risk visibility, performance monitoring, and escalation protocols. These are further mapped against best practices to quantify maturity levels and assess risk exposure. Based on the findings, a set of strategic recommendations is proposed across five dimensions: governance, process, technology, compliance, and culture. These are structured into a phased implementation roadmap to support DBG’s efforts in achieving operational resilience and regulatory alignment. The thesis contributes both to academic understanding of TPRM in highly regulated environments and to practical enhancements for financial institutions operating under European supervision.