Eldorado - Repository of the TU Dortmund
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Autonomic systems
(2025) Sterritt, Roy; Steffen, Bernhard; Hinchey, Mike; Wirsing, Martin
The complexity of today’s and tomorrows’ computer-based/cyber-physical systems-of-systems, consisting of very large numbers of elements, must be increasingly autonomous in order to carry out their complex tasks without constant human input. To reach those increasing levels of autonomy, the elements themselves must have the capability to self-manage, that being; self-configure, self-heal, self-optimise and self-protect through the abilities of self-awareness, self-situation (context and environment-awareness), self-monitoring and self-adjusting. This self-management specialisation of Autonomy, is Autonomicity, or the field of Autonomic Computing.
The cumulative contribution of this thesis is several-fold; adding to IBM’s Autonomic Computing initiative by specifying the self-* properties as an Autonomic Computing quality tree and redefining the Autonomic Element and Architecture with these self-* properties and tighter scoping engineering rules;. by engineering dynamics within autonomic responses and multiple loops of control, such as reflex reactions among the autonomic managers and architecture through “Pulse Monitoring” and the associated artifact Pulse Monitor (PBM) to provide that autonomic reflex reaction; engineering such developments to Autonomic Personal Computing; engineering such to NASA and their future (SWARM based) concept missions; engineering such to Autonomic Communications; ‘Inventing’ the field of Apoptotic Computing, inspired by biological cellular pre-programmed ‘self-destruct’ behaviour, as the ultimate security/trust mechanism for Autonomic Systems. All of these have added to the “vision” of Autonomic Computing, as assessed by IBM and the field’s top-cited researcher.
This mix of Autonomic Computing, Autonomic Communications and Apoptotic Computing leads to a more general umbrella term of Autonomic Systems. Sterritt’s 20+ years of Autonomic Computing research with academia and industry has led to more ‘Engineering Self-Management into Computer-Based Systems’ than can be covered in this accumulative thesis, so much so that Autonomic-* becomes the norm with a hypothesis that ‘all Computer-Based Systems Should be Autonomic’. This view results in many future paths for this research, both in improving the paradigm itself and applying/engineering it to other fields. That said, the last 20+ years of Autonomic Systems (AS) has been mostly an (Software and Systems) Engineering accomplishment. As a field, its next level future growth may be part of the latest A.I. incarnation – Generative AI, as it evolves towards Artificial General Intelligence (AGI). Essentially, AS may be considered advanced automation. Gen-AI AS may provide accelerated AI automation in the next decade performing domain-bounded tasks that exhibit three fundamental characteristics: autonomy, learning, and agency and have an equivalent level of industrial success as Gen-AI.
Characterisation of protein structure and dynamics by NMR spectroscopy and computational methods
(2025) Söldner, Benedikt; Linser, Rasmus; Schäfer, Lars
Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for studying the structure and dynamics of proteins. In contrast to almost all other experimental techniques, NMR spectroscopy facilitates the elucidation of site-specific protein dynamics on various timescales, making it an indispensable tool for structural biology of proteins. In the first chapter, the theory of NMR spectroscopy is introduced and an overview of frequently used NMR spectroscopic methods for studying protein structure and dynamics is given. In addition, an introduction about molecular dynamics (MD) simulations, a technique for studying protein dynamics on an atomic scale used for explaining the dynamics detected by NMR spectroscopy as well as constituting a technique for structure determination of proteins based on observables from NMR spectroscopy, is given.
In chapter 2 – 5, the four major projects investigated for my Ph. D. are presented. In chapter 2, a newly developed method for determining accurate distances from 1H-detected solid-state NMR spectroscopy is presented and demonstrated by structure determination and restrained MD simulations of the chicken α-spectrin SH3 domain. In the project presented in chapter 3, microsecond-timescale dynamics of a small-molecule ligand bound to the active site of the human carbonic anhydrase II (hCAII) was revealed with solid-state NMR spectroscopy, where my contribution consisted in determining the origin of the dynamics on an atomic level using MD simulations. In the project shown in chapter 4, the influence of salt concentration on the protein dynamics was investigated by NMR spectroscopy and MD simulations. In the project presented in chapter 5, the secondary-messenger-induced allosteric modulation of conformational loop dynamics in the PII-like protein A (PstA) is investigated. In addition to the modulation of the spatial properties of the 30-residue long loops, in absence of the ligand, also slow µs-ms timescale dynamics in the core of PstA are revealed.
From regulatory data to quantitative investment signals in equity markets
(2025) Schroeder, Jan L.; Posch, Peter N.; Hellmanzik, Christiane
This dissertation presents five empirical studies that leverage regulatory disclosures filed with the U.S. Securities and Exchange Commission (SEC) as primary information sources to identify structural patterns, anomalies, and actionable signals for equity portfolio strategies. First, a large-scale exploratory analysis of the EDGAR database covers over 15 million disclosures from more than 800,000 entities between 1994 and 2024, revealing persistent declines in public company filings, rising private capital activity, pronounced seasonal trends in insider trading and disclosure volume, and filing surges preceding major market events. Notably, just 2% of form types account for 80% of annual submissions, highlighting concentration in regulatory reporting.
Building on these findings, the second study documents a strong seasonal cycle in regulatory disclosure behavior, with winter months showing significantly higher activity across various filing types. This seasonality aligns with the “Halloween effect” in equity markets, where stock returns tend to outperform from November to April. The analysis suggests that patterns in disclosure timing contribute to this well-known market anomaly and are consistent across both U.S. and European markets.
The third study investigates the effectiveness of replicating hedge fund strategies using holdings reported in quarterly SEC Form 13F filings. Analyzing over 150,000 portfolios between 2013 and 2023, it finds that cloned portfolios based on top-quartile hedge fund filings outperform the S&P 500 by 24.3% annually on a risk-adjusted basis, closely mirroring original fund returns when rebalanced at disclosure dates.
The fourth study focuses on market reactions to Item 4.02 disclosures in Form 8-K, which report non-reliance on previously issued financial statements. Examining over 8,000 such filings from 2004 to 2023, it finds these disclosures are associated with significant negative abnormal returns, especially when related to revenue recognition errors or when the disclosure lacks clarity about the impact or magnitude of the misstatement.
Finally, the fifth study develops an equity portfolio construction strategy based on insider trading activity disclosed in SEC Form 4 filings. Using Monte Carlo simulations on 1.8 million insider transactions, the strategy identifies high-conviction insider purchases that deliver a 5-day cumulative abnormal return of 3.4%. The top-performing backtested portfolio consistently outperforms the S&P 500 in both in- and out-of-sample periods, achieving strong risk-adjusted returns.
Together, these studies demonstrate the value of regulatory disclosure data for understanding capital market behavior and for developing robust quantitative investment strategies.
Frühes Probabilistisches Denken im Elementarbereich: Ein Überblick über aktuelle Forschungsschwerpunkte und zentrale Erkenntnisse
(Gesellschaft für Didaktik der Mathematik, 2025) Jaeger, Lena S.; Lüken, Miriam M.
Bereits ab einem Alter von 3 Jahren beginnen Kinder, lange vor dem Schuleintritt, probabilistisch zu denken und entwickeln tragfähige Vorstellungen zu probabilistischen Konzepten. Aufgrund der alltäglichen Relevanz und Bedeutung für die Anschlussfähigkeit im Grundschulunterricht rückt das frühe probabilistische Denken im Elementarbereich zunehmend in den (inter-)nationalen Forschungsfokus. Unser Beitrag präsentiert Auszüge aus einem Literature Review, identifiziert zentrale Forschungsschwerpunkte und stellt wesentliche Studienergebnisse zum frühen probabilistischen Denken junger Kinder vor.
Teil-Ganzes-Verständnis in der Kindertagesstätte alltags- und spielbasiert fördern
(Gesellschaft für Didaktik der Mathematik, 2025) Mette, Tessa; Bruns, Julia
Das Teil-Ganze-Verständnis (TGV) ist zentral für die mathematische Entwicklung und sollte daher bereits im Elementarbereich gefördert werden. Im Vortrag werden erste Ergebnisse aus einer quasi-experimentellen Studie (N = 81) zur Wirksamkeit einer alltags- und spielbasierten Förderung im Vergleich zu einer strukturierten Förderung („Mengen, zählen, Zahlen“) und einer Kontrollgruppe berichtet. Die Förderungen umfassten je neun Einheiten mit einer Dauer von 45 Minuten. Die mathematische Leistung der Kinder wurde im Prä- und Posttest mit dem MARKO-D erfasst.