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Solution Discovery via Reconfiguration for Problems in P
No full textIn the recently introduced framework of solution discovery via reconfiguration [Fellows et al., ECAI 2023], we are given an initial configuration of k tokens on a graph and the question is whether we can transform this configuration into a feasible solution (for some problem) via a bounded number b of small modification steps. In this work, we study solution discovery variants of polynomial-time solvable problems, namely Spanning Tree Discovery, Shortest Path Discovery, Matching Discovery, and Vertex/Edge Cut Discovery in the unrestricted token addition/removal model, the token jumping model, and the token sliding model. In the unrestricted token addition/removal model, we show that all four discovery variants remain in P. For the token jumping model we also prove containment in P, except for Vertex/Edge Cut Discovery, for which we prove NP-completeness. Finally, in the token sliding model, almost all considered problems become NP-complete, the exception being Spanning Tree Discovery, which remains polynomial-time solvable. We then study the parameterized complexity of the NP-complete problems and provide a full classification of tractability with respect to the parameters solution size (number of tokens) k and transformation budget (number of steps) b. Along the way, we observe strong connections between the solution discovery variants of our base problems and their (weighted) rainbow variants as well as their red-blue variants with cardinality constraints.76:176:2
Social policy dynamics in Iran. A critical study of the post-1979 revolutionary era
No full textThis research examines the dynamics of social policies in Iran over the 45 years following the 1979 revolution. During this period, welfare policies have fluctuated significantly and been subject to various contradictory analyses. Researchers often present conflicting views by focusing on specific aspects. The study critiques the unified approach to understanding the post-revolutionary period, emphasizing the need to consider the complex interplay of various factors. By reviewing changes related to economic indicators, socio-political factors, and dominant ideologies, seven distinct phases were identified:
1. Mustazafin Matters, Revolution, and War (1979-1988)
2. The Neoliberal Shift (1989-1996)
3. The Social Security System (1997-2004)
4. Welfare Expansion based on Oil Revenues (2005-2008)
5. Cash Transfer under Intensified Sanctions (2009-2012)
6. Unstable Recovery and Hope (2013-2016)
7. Despair and Drastic Adjustments (2017–2024)
Each phase exhibits unique policy characteristics, providing a comprehensive understanding of social policy changes. This approach enables comparative analysis, revealing that social security has been the most dynamic policy area, while housing policies have remained relatively passive.
The research highlights the instability of the post-revolutionary period, marked by fluctuating government revenues, external conflicts, and varying international relations. The methodology employs qualitative analysis, supplemented by secondary data and expert interviews, to ensure robustness and validity. The findings indicate that multiple internal and external events and significant fluctuations in Iran's conditions preclude a single-theory explanation for the post-revolutionary period. Social policy changes in Iran result from these diverse and dynamic contextual factors rather than a specific collective will or the agenda of any particular political party or social movement.Deutsche Forschungsgemeinschaft (DFG)2
Salt-driven fibrinogen self-assembly into nanofibers and their biofunctionality
No full textFibrinogen self-assembly plays a critical role in tissue engineering and wound healing. While various methods generate fibrin-like networks, salt-induced fibrinogen self-assembly has gained attention due to its efficiency and solvent-free nature. However, the underlying mechanism remains unclear. This thesis investigates fibrinogen-salt interactions to develop reproducible nanofiber scaffolds for biomedical applications.
The study explores how different salts influence fibrinogen self-assembly in vitro. Initial findings show that salts in non-denaturing buffers are crucial for fiber formation. Further analysis examines the effects of divalent (Ca²⁺, Mg²⁺, Zn²⁺, Cu²⁺) and monovalent (Na⁺, K⁺, NH₄⁺) salts. Contrary to expectations, divalent ions, essential in clot formation, did not induce fibrinogen fiber formation, instead producing smooth layers. In contrast, monovalent salts in phosphate-buffered saline (PBS) triggered fibrinogen self-assembly after drying.
To understand this effect, different monovalent cation-anion combinations were tested. Results revealed a direct correlation between salt composition and fibrinogen morphology, with kosmotropic cations (e.g., Na⁺) and kosmotropic anions (e.g., H₂PO₄⁻) being most effective. These findings align with Hofmeister effects, suggesting that specific ion binding and hydration shell modulation drive fibrillogenesis.
Additionally, the study examines fibrinogen’s influence on sodium chloride crystal morphology, revealing a reciprocal interaction between proteins and salts. The final analysis assesses the biocompatibility of salt-induced fibrinogen nanofibers, showing increased platelet activation on fibrous surfaces.
In conclusion, this research elucidates the role of salts in fibrinogen self-assembly, offering insights into ion-driven protein structuring. These findings provide insights into controlled fibrinogen nanofiber fabrication, enhancing biomaterial applications in tissue engineering and wound healing
Machine-learning based observational cloud products for process-oriented climate model evaluation
No full textThe importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate models (GCMs). Uncertainties from the representation of clouds in GCMs have been identified as a leading cause of inter-model spread in climate projections.
Our current understanding of clouds and the processes relevant to their formation and effect on climate is informed partly by observations from remote sensing instruments aboard orbital satellites. This thesis introduces new methods of characterizing clouds from space with the help of machine learning and neural networks. The purpose of these methods is to improve the understanding of and reduce the uncertainties in climate projections by providing satellite products that are objectively interpretable and consistently comparable to GCM output.
The methods explored in this thesis highlight that machine learning and especially neural networks have the potential to improve multiple aspects of climate science. The presented results show that cloud classes can be reliably obtained from low-resolution data to improve their interpretability. They further show that comparison between climate models and observations can potentially be simplified with machine learning
Wir in der Pflege
No full textRUnd 16000 Beschäftigte arbeiten im Land Bremen in der Pflege - im Krankenhaus, in einer Pflegeeinrichtung oder in der ambulanten Pflege. "Wir in der Pflege" soll diesen Kolleginnen und Kollegen als Ratgeber im Berufsalltag dienen und mgölichst knappe und verständliche Antworten auf eine Vielzahl von rechtlichen Fragen liefern. Die Themen reichen von A wie Arbeitskleidung, über D wie Dienstwagen, R wie Rufbereitschaft bis hin zu Z wie Zuschläge
Eine netzstabilisierende Regelstrategie für Windenergieanlagen zur Bereitstellung von kraftwerksäquivalenten Systemdienstleistungen
No full textZur Wahrung eines stabilen Netzbetriebs bei einem immer weiter steigenden Anteil an installierten Windenergieanlagen ist ein Paradigmenwechsel in der bisher eingesetzten, ausschließlich auf eine maximale Windleistungsentnahme abzielenden MPPT (Maximum Power Point Tracking) Regelstrategie unabdingbar. Hierbei ist es erforderlich, dass die bei einer gewünschten klimaneutralen Energieproduktion wegfallenden Systemdienstleistungen konventioneller, CO2 emittierender Kraftwerke adäquat ersetzt werden. Aufgrund dessen wird in der vorliegenden Dissertation die netzstabilisierende GPPT (Grid-demanded Power Point Tracking) Betriebsweise für eine häufig installierte Windenergieanlagentopologie mit Vollumrichter und permanentmagneterregtem Synchrongenerator entwickelt und experimentell erfolgreich validiert.
Zur praktischen Validierung wird ein 20kW-Prüfstand mit einem zentralen Regelungssystem aufgebaut. Dieser besteht im Wesentlichen aus einer realen Windenergieanlage ohne Turm und Windrotor, weshalb ein Motor den Generator für die Nachbildung des Rotorverhaltens antreibt. Zusätzlich wird ein übergeordnetes Stromnetz emuliert, um damit die stabilisierende Auswirkung der Einspeisung auf die Netzgrößen bei einem hohen Anteil an Windenergie herauszustellen.
Am Prüfstand wird nachgewiesen, dass die Windenergieanlage analog zu konventionellen Kraftwerken eine Momentan- und Primärreserve und zusätzlich die Sekundärreserve von aktuellen Regelkraftwerken bereitstellt. Die eingespeiste Leistung wird dabei auch rotorseitig korrekt nachgestellt, was zu veränderten Arbeitspunkten des Windrotors führt. Darüber hinaus bietet das GPPT-Verfahren eine von Netzbetreibern geforderte Spannungsstützung durch eine entsprechende Adaption des eingespeisten Blindstroms sowie das Durchfahren von Unterspannung (LVRT) bei Netzfehlern. Außerdem wird die Schwarzstartfähigkeit im Inselbetrieb untersucht und simulatorisch veranschaulicht
The impact of wear, contamination and substrate conditions on the pull off force of adhesive microstructures in relation to sustainable manufacturing
No full textMushroom shaped adhesive microstructures (MSAMS) provide the potential to grasp a wide variety of objects with minimal energy usage and are therefore particularly interesting for an automated and sustainable production. Investigating the influence of wear, contamination and substrate surface conditions on the adhesive force is crucial to assess their usability in non-laboratory conditions. In this work, the pull off force of MSAMS on additively manufactured specimens is measured during multiple cycles. The results show, that different surface finishes can lead to major changes of the resulting adhesive force. Adhesion is prevented by particle contamination but can be restored by cleaning
Transforming Web Knowledge into Actionable Knowledge Graphs for Robot Manipulation Tasks
No full textOne of the visions in AI based robotics are household robots that can autonomously handle a variety of meal preparation tasks. Based on this scenario, we present a best practice tutorial on how to create actionable knowledge graphs that a robot can use for execution of task variations of cutting actions. We implemented a solution for this task that integrates all necessary software components in the framework of the robot control process. In the context of this tutorial, we focus on knowledge acquisition, knowledge representation and reasoning, and simulating robot action execution, bringing these components together into a learning environment that – in the extended version – introduces the whole control process of Cognitive Robotics. In particular, the Tutorial will detail necessary concepts a knowledge graph should include for robot action execution, how web knowledge can be automatically acquired for the domain of cutting fruits, and how the created knowledge graph can be used to let robots execute tasks like slicing a cucumber or quartering an apple. The learning environment follows an immersive approach, using a physics-based simulation environment for visualization purposes that helps to illustrate the concepts taught in the tutorial
Strukturelles Versagen eines Wälzlagerstahls 42CrMo4 - Untersuchungen von Ermüdung und Rissfortschritt und Erarbeitung einer realistischen Lebensdauervorhersage
No full textDie Ermüdungsuntersuchungen am Stahl 42CrMo4 bieten signifikante Erkenntnisse für seine Anwendung in kritischen Komponenten, insbesondere in Blattlagern von Windenergieanlagen. Für diese Studie werden Proben direkt aus einem ungenutzten Lagerring entnommen und diversen Tests unterzogen, um Materialkennwerte zu erheben. Dabei werden unterschiedliche Wärmebehandlungszustände des Stahls untersucht. Die Gesamtlebensdauer von Proben wird durch die Ermüdungsprüfung in Umlaufbiege- und Zug-Druck-Versuchen ermittelt. Dabei wird auch der Einfluss von Oberflächenkorrosion untersucht. Weiterhin werden Rissfortschrittsuntersuchungen an homogen gehärteten als auch an teilgehärteten Proben durchgeführt. Die ermittelten Rissgeschwindigkeitskurven dienen als Basis für eine Finite Elemente (FE) Simulationen des Rissfortschritts. Die Simulation wird zunächst an Testgeometrien, analog der im Experiment genutzten Proben, validiert. Die Korrelation von experimentell und simulativ gewonnenen Daten ermöglicht anschließend die Abschätzung von Rissinitiierungsdauern abhängig von der Belastungshöhe. Weiterhin lässt sich durch die Übertragung der Rissfortschrittssimulation auf ein Blattlager die Lebensdauer von vorgeschädigten Bauteilen vorhersagen. Dieser Ansatz kann somit einen Beitrag zur Optimierung von Konstruktions- und Wartungsstrategien von Blattlagern leisten
Unrelated Machine Scheduling in Different Information Models
No full textUnrelated machines are an abstraction of many scheduling environments appearing in practical applications, where every job may be processed at a different speed on every machine. We study different optimization problems for scheduling unrelated machines in three categories of information models, which determine the amount of information of an instance an algorithm has access to. First, we consider the offline model, where all information about the instance are known to an algorithm. We show a new connection between minimizing the makespan on unrelated machines and the closely related Santa Claus problem regarding their polynomial-time approximability. Second, we consider the online model, where an algorithm has to schedule jobs over time while coping with uncertainty about the instance. In particular, we consider non-clairvoyant scheduling, where an algorithm has no knowledge about a job's processing requirements until it completes. We prove close-to-optimal competitive ratios for this problem on unrelated machines and for even more general scheduling environments using an elegant and natural allocation rule from economics. Finally, in the third part of this thesis, we study the learning-augmented information model. In this recently emerging framework, an algorithm is given access to a potentially erroneous prediction, which potentially give an algorithm additional information to output a better solution. We present new prediction models for various scheduling problems, and design and analyze learning-augmented algorithms