Publikationsserver der Westfälischen Hochschule
Not a member yet
2819 research outputs found
Sort by
Disrupted Vessels—Connected Voices: Why Patient Partnership and Cross-Disease Collaboration Are Essential for Accelerating HHT Research
No full textRare vascular diseases such as hereditary haemorrhagic telangiectasia (HHT) represent a big challenge in biomedicine: complex pathomechanisms, limited patient material, and fragmented research communities slow down therapeutic progress. We argue that two elements are pivotal to bypass this problem. First, genuine partnership with patients—ranging from biospecimen donation to agenda setting—can unlock critical resources and align research with real-world needs. Second, molecular intersections between HHT and related pathologies call for coordinated, cross-disease programmes rather than isolated efforts. Recent multi-stakeholder gatherings hosted by patient organisations in Germany and elsewhere, such as the Second Scientific Symposium by the German HHT self-help group (Morbus Osler Selbsthilfe e.V.) in May 2025, have shown that when clinicians, basic scientists from different disciplines, and affected families co-design research questions, novel in vitro models can be generated more accurately, and pragmatic clinical trials emerge. Here, we outline actual opportunities for patient-integrated cellular model systems, shared biobanking, and comparative approaches across vascular malformation syndromes. In our opinion, letting informed and well-organised patient communities assemble such meetings opens unique opportunities twofold: on the one hand, the field can finally break out of its disease-specific silos; on the other hand, the development of novel HHT therapies could be accelerated by learning from progress in related pathologies
PySSA for Windows: End-User Protein Structure Prediction and Visual Analysis with ColabFold and PyMOL
No full textPySSA (Python rich client for visual protein Sequence to Structure Analysis) for Windows is a comfortable open Graphical User Interface (GUI) application combining the protein sequence to structure prediction capabilities of ColabFold with the open-source variant of the molecular structure visualization and analysis system PyMOL to make both available to the scientific end-user. PySSA enables the creation and sharing of workflow projects that comprise defined protein 3D structure predictions from their amino acid sequence, protein 3D structure alignments, as well as their visual analysis with distance diagrams or hotspot inspection. All operations can be conveniently performed by scientists without specialized computer skills or even programming knowledge on their local Windows computers, without the need for powerful GPU hardware. Thus, PySSA can help make protein structure prediction more accessible for end-users in scientific research areas like protein chemistry or molecular biology. In addition, the application is well-suited for educational purposes due to its user-friendliness and low learning curve. PySSA is openly available on GitHub, alongside a convenient installer executable for the Windows operating system: https://urban233.github.io/PySSA/install.html. To demonstrate its capabilities, the usage of PySSA in a protein mutation study on the protein drug Bone Morphogenetic Protein 2 (BMP2) is described: the structure prediction results indicate that the previously reported BMP2-2Hep-7M mutant, which is intended to be less prone to aggregation, does not exhibit significant spatial rearrangements of amino acid residues interacting with the receptor
Dark LLMs im Kontext von OSINT: Prototypentwicklung und Analyse der Anwendung für die polizeiliche Gefahrenabwehr und Strafverfolgung in Nordrhein-Westfalen
No full textIn dieser Arbeit wird der Einsatz von Dark LLMs zur Durchführung von OSINT-Recherchen im Rahmen der polizeilichen Gefahrenabwehr und Strafverfolgung in Nordrhein-Westfalen analysiert. Hierfür wird zunächst eine erste wissenschaftliche Definition des Begriffs Dark LLM erstellt. Anschließend werden relevante Dark LLMs vorgestellt, auf deren Gefahren aufmerksam gemacht und geeignete Gegenmaßnahmen erörtert. Danach erfolgt eine rechtliche Einordnung anhand mehrerer Gesetzestexte. Dann wird eine prototypische Entwicklung eines LLMs durchgeführt, welches für OSINT-Recherchen eingesetzt werden kann. Die rechtliche Analyse und die technische Implementierung ergeben, dass der Einsatz eines Dark LLMs in der polizeilichen Arbeit unzulässig ist. Zudem wird ein polizeiliches LLM nach der KI-Verordnung sowohl als Hochrisiko-KI-System als auch als KI-Modell mit allgemeinem Verwendungszweck eingestuft, wodurch sich etliche Pflichten ergeben, die ein LLM in ihrer Funktion einschränken. Trotz dieser Einschränkungen ist ein Einsatz eines solchen LLMs sinnvoll, denn dieses kann viele OSINT-Informationen sekundenschnell zu einer gebündelten Antwort zusammenfassen, was die polizeiliche Arbeit erheblich erleichtert. Durch allgemeines OSINT-Training und die Anbindung weiterer OSINT-Quellen kann die Qualität der Antwort deutlich verbessert werden.This paper analyses the use of dark LLMs to conduct OSINT research in the context of police hazard prevention and law enforcement in North Rhine-Westphalia. To this end, an initial scientific definition of the term dark LLM is first established. Subsequently, relevant dark LLMs are presented, their dangers are highlighted, and appropriate countermeasures are discussed. This is followed by a legal classification based on several legal texts. Then a prototype LLM is developed that can be used for OSINT research. The legal analysis and technical implementation show that the use of a dark LLM in police work is not permitted. In addition, according to the AI Regulation, a police LLM is classified as both a high-risk AI system and an AI model for general use, which results in a number of obligations that restrict the function of an LLM. Despite these restrictions, the use of such an LLM is useful because it can summarise a lot of OSINT information into a bundled response in a matter of seconds, which makes police work considerably easier. Through general OSINT training and the connection of additional OSINT sources, the quality of the response can be significantly improved
Corporate debt ratios and managerial personality traits: A content analysis of chief executive officers’ speeches at annual general meetings
No full textThis study contributes to the literature by analysing the joint association of managerial overconfidence, certainty, narcissism, and the Big Five personality traits with debt ratios in the institutional setting of the German two-tier system. Moreover, it provides insights into how corporate governance quality moderates the effects of personality. The analysis relied on the chief executive officers’ (CEOs’) speeches at annual general meetings (AGMs) that were voluntarily disseminated, a novel data source. Managers’ personality traits were measured using software-aided content analysis, and their impact on the debt ratio was analysed using panel regressions. Consistent with previous studies, the debt ratios of German issuers are significantly and positively related to the proxies of managerial certainty and narcissism. However, their model inclusion contributes only marginally to explanatory power. Conversely, the coefficients of the proxies for the Big Five personality traits remained statistically non-significant. Moreover, a significantly negative relationship between debt ratios and the interaction term between a proxy for corporate governance quality and managerial certainty is observed that corresponds to the risk-mitigating impact of corporate governance
Nachhaltigkeitsreporting für kleine und mittlere Unternehmen
No full textDas Nachhaltigkeitsreporting ist und bleibt für viele Unternehmen ein wichtiges, aktuelles und dynamisches Thema - auch politisch gesehen.
Der Studienbericht ist eine Fortführung der im Jahr 2023 durchgeführten empirischen Erhebung zum Stand der digitalen Nachhaltigkeitsberichterstattung in Unternehmen des westlichen Münsterlandes und fand im Rahmen des EFRE-Projekts "DiNaOpt4KMU" statt. Ein Vergleich der Ergebnisse zur ersten Ausgabe zeigt unter anderem, dass ein Großteil der Unternehmen sich im Bereich Nachhaltigkeit positioniert und weiterentwickelt haben. Zudem konnten in der zweiten Auflage Entwicklungen in der Region Westmünsterland aufgezeigt und bewertet werden. Es konnte eine Klassifizierung der teilgenommen Unternehmen in „Neueinsteiger, Einsteigende und Fortgeschrittene“ erfolgen.
Eine weitere Studienauflage ist für das Jahr 2026 in Planung
Power plays: Surfacing the hidden currents in entrepreneurial ecosystems
No full textEntrepreneurial ecosystem (EE) research has flourished, yet it continues to overlook how power shapes who can mobilize resources, legitimacy, and opportunity within these systems. This paper reframes power as a constitutive, and not incidental, dimension of ecosystem functioning. Integrating insights from Pfeffer's resource-dependence theory with Foucault's and Bourdieu's social perspectives, we develop a Power-Sensitive Entrepreneurial Ecosystem Framework (PSEEF) that conceptualises power across micro (entrepreneurial identity), meso (network structures), and macro (institutional governance) levels. Our key insight is that power dynamics are the hidden mechanisms through which EEs determine who gains visibility and support, and whose ventures remain peripheral. Recognising these mechanisms enables scholars and practitioners to understand ecosystems not merely as collections of resources but as evolving arenas of legitimacy and control. The framework provides actionable tools for ecosystem leaders to diagnose and rebalance inequalities, advancing both the analytical and practical agenda for inclusive and high-performing EEs
EuGH: E-Commerce/Verbraucherrecht: Hinweispflicht auf Bonitätsprüfung in Werbung [Anmerkung zum EuGH-Urteil vom 15.5.2025, C 100/24]
No full textKonkretisierung der FluggastrechteVO : EuGH, LG Frankfurt a. M., Urteil vom 16.1.2025 – C-516/23 ; [Anmerkungen]
No full textRobust & Reliable Automated Feedback Using Tree Edit Distance for Solving Open Response Mathematical Questions
No full textAs the student population becomes increasingly heterogeneous, providing effective feedback is crucial for personalized education. However, human feedback is resource-intensive, while large language models can be unreliable. Our method bridges this gap by offering informative, similarity-based feedback on mathematical inputs. In an experiment with 207 students, we found that this approach encourages engagement, facilitates the completion of harder exercises, and reduces quitting after incorrect inputs. Compared to traditional feedback mechanisms that struggle with unforeseen error patterns, our method increases student perseverance and confidence. By balancing reliability, resources, and robustness, our solution meets the diverse needs of contemporary students. With its potential to enhance self-learning and student outcomes, this research contributes to the growing conversation on personalized education and adaptive learning systems