Aging is driven by damage accumulation leading to a decline in function over time. In single-cell systems, in addition to this damage accumulation within individuals, asymmetric damage partitioning at cell division can play a crucial role in shaping demographic aging patterns. Despite experimental single-cell studies that provide quantitative data at the molecular and demographic levels, the integration of a complementary theory explaining how cellular damage production and asymmetric partitioning propagate and influence demographic patterns is still lacking. Here, we present a generic and flexible damage model using a stochastic differential equation approach that incorporates stochastic damage accumulation and asymmetric damage partitioning during cell divisions. We formulate an analytical approximation linking cellular and damage parameters to demographic aging patterns along mother cell lineages. Interestingly, the lifespan of cells follows an inverse Gaussian distribution, whose underlying properties derive from cellular and damage parameters. We demonstrate how stochasticity (noise) in damage production, asymmetry in damage partitioning, and division frequency shape lifespan distribution. Confronting the model with various empirical E. coli mother machine data reveals nonexponential scaling in mortality rates, a scaling that cannot be captured by classical Gompertz-Makeham models. Our findings provide a deeper understanding of how fundamental processes contribute to cellular damage dynamics and generate demographic patterns. Our damage model's generic nature offers a valuable framework for investigating aging in diverse biological systems.

Pre-mRNA splicing of intron-containing transcripts is a prerequisite to yield functional mRNAs. Its regulation results in mRNA sequence or amount variations. How splicing shapes gene expression and physiology in microorganisms is not well understood beyond the common model budding yeast. This is also true for Candida albicans, one of the most prevalent human fungal pathogens. Recent studies highlight the impact of RNA processing on its virulence, providing insights into potential drug targets.

Das Workbook "Politisches Weiterdenken" bietet einen innovativen Ansatz zur politischen Bildung im digitalen Zeitalter. Es verknüpft theoretische Grundlagen mit praxisorientierten Methoden und stellt generative KI als Werkzeug für die Förderung von Zukunftskompetenzen und demokratischer Teilhabe vor. Zentrale Themen sind Futures Literacy, Truth Literacy, Complexity Literacy und der reflektierte Einsatz von KI in Bildungskontexten. Angesichts populistischer Herausforderungen und gesellschaftlicher Transformationen unterstützt das Workbook Lehrende und Lernende dabei, kritisches Denken und Selbstwirksamkeit zu stärken. Neben theoretischen Impulsen enthält es konkrete Szenarien, Mega-Prompts und Anleitungen, um politische Entwicklungen auf verschiedenen gesellschaftlichen Ebenen zu reflektieren und praxisnah zu bearbeiten.

Efficient crew scheduling is essential for cost-effective and reliable public transportation operations. This paper presents a mathematical model for an extensive duty network and introduces an advanced scheduling approach that integrates column generation and dynamic network generation to iteratively construct and refine duty schedules. The column generation method is used to compute an upper bound and generate an initial duty schedule, while dynamic network generation iteratively improves the schedule, ensuring feasibility and enabling cost reductions. The proposed approach balances computational efficiency with solution quality, allowing for early termination with near-optimal results.

Both the extensive and dynamic approaches face challenges in computing optimal solutions within a practical runtime. The extensive duty network model benefits significantly from preprocessing, which reduces the complexity of the problem and improves solvability. However, its applicability remains limited for medium to large-scale instances. The dynamic network generation approach, in contrast, efficiently computes high-quality duty schedules in comparably short computational time. It can be used flexibly—either to generate initial feasible solutions, refine existing schedules, or continue iterating until optimality is proven if required.

Despite the challenges in proving optimality, the structured nature of the dynamic network generation approach makes it highly promising for integration with other planning steps, such as timetabling and vehicle scheduling. Future research should further evaluate the integration of dynamic network generation into broader transportation planning frameworks and explore enhancements to improve convergence speed. Overall, while not necessarily the most efficient standalone method for optimizing crew schedules, the dynamic network generation approach offers a powerful and flexible tool for integrated public transportation planning, enabling both efficiency gains and operational adaptability.

Organized domestic workers have recently been at the forefront of political campaigns in Peru to promote legislative reforms concerning forced labor. This empirical case study investigates how the international labor norm concerning forced labor is disseminated, appropriated and politically mobilized at the national level. It thereby examines the dynamics and cultural embedding of the appropriation process and its implications for labor regulation, policy responses and trade union actions. The research delves into the ways and spaces in which international and national actors, the International Labour Organisation and national domestic workers’ trade unions, translate the international norms into their local context, adapt and negotiate them. I argue that the mobilization against forced domestic labor is embedded in the period of reconfiguration of the domestic work sector in Peru and that it is oriented toward internationally institutionalized categorizations and indicators. By employing the theoretical lens of vernacularization, a contextualizing examination of the labor policy actors, their positions and knowledge bases, as well as an analysis of decisive moments, spaces, technologies and developments of communicative actions, enables a sociological understanding of the emergence and manifestation of the mobilization against forced domestic work. The process of vernacularization creates a localized version of international norms that retains elements of the original while incorporating local specificities in the implementation. It creates a space where different forms of knowledge interact – local knowledge and transnational technicized knowledge. This research adds a nuanced understanding of the intersection between internationalization of ‘universal’ labor standards, knowledge production, and trade union mobilization within a feminized and racialized sector.

This open access textbook offers science education researchers a hands-on guide for learning, critically examining, and integrating machine learning (ML) methods into their science education research projects. These methods power many artificial intelligence (AI)-based technologies and are widely adopted in science education research. ML can expand the methodological toolkit of science education researchers and provide novel opportunities to gain insights on science-related learning and teaching processes, however, applying ML poses novel challenges and is not suitable for every research context.

The volume first introduces the theoretical underpinnings of ML methods and their connections to methodological commitments in science education research. It then presents exemplar case studies of ML uses in both formal and informal science education settings. These case studies include open-source data, executable programming code, and explanations of the methodological criteria and commitments guiding ML use in each case. The textbook concludes with a discussion of opportunities and potential future directions for ML in science education.

This textbook is a valuable resource for science education lecturers, researchers, under-graduate, graduate and postgraduate students seeking new ways to apply ML in their work.

„The Social Responsibility of Business is to Increase its Profits” titelt der US-amerikanische Ökonom Milton Friedman 1970. Der Zweck einer Kapitalgesellschaft erschöpft sich danach in der wohlfahrtsmaximierenden Interessenaggregation. Gesamtgesellschaftliche Ziele sind im Kapitalgesellschaftsrecht danach fehl am Platz. Jüngere Entwicklungen strafen diese Unterstellung Lügen. Unternehmen werden nicht nur durch Regulierung als gesellschaftspolitische Akteure in die Pflicht genommen, sondern wollen auch als solche sichtbar sein. Lassen sich Gesellschaftspolitik und Gesellschaftsrecht aber wirklich verbinden, und wo liegen die inneren und äußeren Grenzen dieser Symbiose? Der vorliegende Sammelband beleuchtet das Phänomen „Gute Kapitalgesellschaft“ aus soziologischer, rechtsdogmatischer und -vergleichender Perspektive.

Future quantum technologies such as quantum communication, quantum sensing, and distributed quantum computation, will rely on networks of shared entanglement between spatially separated nodes. In this work, we provide improved protocols and policies for entanglement distribution along a linear chain of nodes, both homogeneous and inhomogeneous, that take practical limitations into account. For a wide range of parameters, our policies improve upon previously known policies, such as the “swap-as-soon-as-possible” policy, with respect to both the waiting time and the fidelity of the end-to-end entanglement. This improvement is greatest for the most practically relevant cases, namely, for short coherence times, high link losses, and highly asymmetric links. To obtain our results, we model entanglement distribution using a Markov decision process, and then we use the 𝑄-learning reinforcement-learning (RL) algorithm to discover alternative policies. These policies are characterized by dynamic, state-dependent memory cutoffs, and collaboration between the nodes. In particular, we quantify this collaboration between the nodes. Our quantifiers tell us how much “global” knowledge of the network every node has, specifically, how much knowledge two distant nodes have of each other’s states. In addition to the usual figures of merit, these quantifiers add an extra dimension to the performance analysis and practical implementation of quantum repeaters. Finally, our understanding of the performance of large quantum networks is currently limited by the computational inefficiency of simulating them using RL or other optimization methods. The other main contribution of our work is to address this limitation. We present a method for nesting policies in order to obtain policies for large repeater chains. By nesting our RL-based policies for small repeater chains, we obtain policies for large repeater chains that improve upon the swap-as-soon-as-possible policy, and thus we pave the way for a scalable method for obtaining policies for long-distance entanglement distribution under practical constraints.

Tensor networks capture large classes of ground states of phases of quantum matter faithfully and efficiently. Their manipulation and contraction has remained a challenge over the years, however. For most of the history, ground state simulations of two-dimensional quantum lattice systems using (infinite) projected entangled pair states have relied on what is called a time-evolving block decimation. In recent years, multiple proposals for the variational optimization of the quantum state have been put forward, overcoming accuracy and convergence problems of previously known methods. The incorporation of automatic differentiation in tensor networks algorithms has ultimately enabled a new, flexible way for variational simulation of ground states and excited states. In this work we review the state-of-the-art of the variational iPEPS framework, providing a detailed introduction to automatic differentiation, a description of a general foundation into which various two-dimensional lattices can be conveniently incorporated, and demonstrative benchmarking results.

Aimed at a more realistic classical description of natural quantum systems, we present a two-dimensional tensor network algorithm to study finite temperature properties of frustrated model quantum systems and real quantum materials. For this purpose, we introduce the infinite projected entangled simplex operator ansatz to study thermodynamic properties. To obtain state-of-the-art benchmarking results, we explore the highly challenging spin-1/2 Heisenberg antiferromagnet on the Kagome lattice, a system for which we investigate the melting of the magnetization plateaus at finite magnetic field and temperature. Making a close connection to actual experimental data of real quantum materials, we go on to studying the finite temperature properties of Ca10⁢Cr7⁢O28. We compare the magnetization curve of this material in the presence of an external magnetic field at finite temperature with classically simulated data. As the first theoretical tool that incorporates both thermal fluctuations as well as quantum correlations in the study of this material, our work contributes to settling the existing controversy between the experimental data and previous theoretical works on the magnetization process.

Known mappings that encode fermionic modes into a bosonic qubit system are non-local transformations. In this paper we establish that this must necessarily be the case, if the locality graph is complex enough (for example for regular 2d lattices). In particular we show that, in case of exact encodings, a fully local mapping is possible if and only if the locality graph is a tree. If instead we allow ourselves to also consider operators that only act fermionically on a subspace of the qubit Hilbert space, then we show that this subspace must be composed of long range entangled states, if the locality graph contains at least two overlapping cycles. This implies, for instance, that on 2d lattices there exist states that are of low depth from the fermionic point of view, while in any encoding require a circuit of depth at least proportional to the system size to be prepared.

We provide practical and powerful schemes for learning properties of a quantum state using a small number of measurements. Specifically, we present a randomized measurement scheme modulated by the depth of a random quantum circuit in one spatial dimension. This scheme interpolates between two known classical shadows schemes based on random Pauli measurements and random Clifford measurements. We focus on the regime where depth scales logarithmically in the system size and provide evidence that this retains the desirable sample complexity properties of both extremal schemes while also being experimentally feasible. We present methods for two key tasks; estimating expectation values of certain observables from generated classical shadows and, computing upper bounds on the depth-modulated shadow norm, thus providing rigorous guarantees on the accuracy of the output estimates. We achieve our findings by bringing together tools from shadow estimation, random circuits, and tensor networks.

Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where {parameterized quantum circuits} (PQCs) are used as learning models. These PQC models have a rich structure which suggests that they might be amenable to efficient dequantization via {random Fourier features} (RFF). In this work, we establish necessary and sufficient conditions under which RFF does indeed provide an efficient dequantization of variational quantum machine learning for regression. We build on these insights to make concrete suggestions for PQC architecture design, and to identify structures which ar

Perturbative gadgets are a tool to encode part of a Hamiltonian, usually the low-energy subspace, into a different Hamiltonian with favorable properties, for instance, reduced locality. Many constructions of perturbative gadgets have been proposed over the years. Still, all of them are restricted in some ways: Either they apply to some specific classes of Hamiltonians, they involve recursion to reduce locality, or they are limited to studying time evolution under the gadget Hamiltonian, e.g., in the context of adiabatic quantum computing, and thus involve subspace restrictions. In this work, we fill the gap by introducing a versatile universal, nonrecursive, nonadiabatic perturbative gadget construction without subspace restrictions, that encodes an arbitrary many-body Hamiltonian into the low-energy subspace of a three-body Hamiltonian and is therefore applicable to gate-based quantum computing. Our construction requires 𝑟⁢𝑘 additional qubits for a 𝑘-body Hamiltonian comprising 𝑟 terms. Besides a specific gadget construction, we also provide a recipe for constructing similar gadgets, which can be tailored to different properties, which we discuss.

This article examines the role of knowledge production in shaping racialized religious difference and its entanglement with governmental interventions, focusing on C.H. Becker’s contributions to Islamic Studies in the 19th and 20th centuries. Situated within the colonial–imperial context of the German Empire, C.H. Becker’s work exemplifies how secular knowledge framed Islam as both a problem and a resource for governance. His framing of religious difference reveals how tolerance operated as a political technique—performing inclusion while simultaneously reinforcing control. The analysis explores the epistemological foundations of C.H. Becker’s approach, demonstrating the intersection of Orientalism, secularism, and racism in producing religious difference and translating academic inquiry into political regulation. By juxtaposing the “Islamfrage” with the “Judenfrage” of the 19th century, this study reveals shared patterns in the regulation of racialized religious difference through secular frameworks, where tolerance functions as both a mechanism of inclusion and a tool of control. These processes not only defined normatively but also aligned knowledge production with national and colonial strategies, illustrating how C.H. Becker’s conceptualization of Islampolitik is characterized by broader dynamics of liberal governance and colonial control.

Many-body localization in disordered systems in one spatial dimension is typically understood in terms of the existence of an extensive number of (quasi) local integrals of motion (LIOMs) which are thought to decay exponentially with distance and interact only weakly with one another. By contrast, little is known about the form of the integrals of motion in disorder-free systems which exhibit localization. Here, we explicitly compute the LIOMs for disorder-free localized systems, focusing on the case of a linearly increasing potential. We show that while in the absence of interactions, the LIOMs decay faster than exponentially, the addition of interactions leads to the formation of a slow-decaying plateau at short distances. We study how the localization properties of the LIOMs depend on the linear slope, finding that there is a significant finite-size dependence, and present evidence that adding a weak harmonic potential does not result in typical many-body localization phenomenology. By contrast, the addition of disorder has a qualitatively different effect, dramatically modifying the properties of the LIOMS.

Non-equilibrium molecular dynamics (NEMD) simulations of fluid flow have highlighted the peculiarities of nanoscale flows compared to classical fluid mechanics; in particular, boundary conditions can deviate from the no-slip behavior at macroscopic scales. For fluid flow in slit-shaped nanopores, we demonstrate that surface morphology provides an efficient control on the slip length, which approaches zero when matching the molecular structures of the pore wall and the fluid. Using boundary-driven, energy-conserving NEMD simulations with a pump-like driving mechanism, we examine two types of pore walls—mimicking a crystalline and an amorphous material—that exhibit markedly different surface resistances to flow. The resulting flow velocity profiles are consistent with Poiseuille theory for incompressible, Newtonian fluids when adjusted for surface slip. For the two pores, we observe partial slip and no-slip behavior, respectively. The hydrodynamic permeability corroborates that the simulated flows are in the Darcy regime. However, the confinement of the fluid gives rise to an effective viscosity below its bulk value; wide pores exhibit a crossover between boundary and bulk-like flows. In addition, the thermal isolation of the flow causes a linear increase in fluid temperature along the flow, which we relate to strong viscous dissipation and heat convection, utilizing conservation laws of fluid mechanics. Noting that the investigated fluid model does not form droplets, our findings challenge the universality of previously reported correlations between slippage, solvophobicity, and a depletion zone. Furthermore, they underscore the need for molecular-scale modeling to accurately capture the fluid dynamics near boundaries and in nanoporous materials, where macroscopic models may not be applicable.

We developed a three-dimensional (3D) polyglycerol–poly(ethylene glycol)-based hydrogel as a new biosensing matrix for affinity analysis by surface plasmon resonance to enable a high loading of ligands for small molecule analysis while lacking a carbohydrate structure to reduce nonspecific binding. The hydrogel was synthesized by cross-linking a polyglycerol functionalized with carboxylate and maleimide groups with a dithiolated poly(ethylene glycol) by thiol-click chemistry. We demonstrated that the hydrogel coating enabled a high immobilization capacity of biomolecules and led to less nonspecific binding. Here, the degree of loading with carbonic anhydrase II and the resulting binding signal of acetazolamide were increased by a factor of 5 compared to standard CMD sensors (CM5), and the loading was comparable to CMD sensors specialized for maximum loading (CM7). This high loading capacity, combined with the reduced nonspecific binding due to the missing carbohydrate structure, presents an innovative matrix for a broad application range of surface plasmon resonance (SPR) experiments since no current commercial SPR biosensor combines these two key features.

Ombudschaftliche Beratung in der Kinder- und Jugendhilfe soll die strukturelle Machtasymmetrie der Helfer_innen-Klient_innen-Beziehung ausgleichen, junge Menschen und ihre Familien über ihre Rechte informieren und sie darin unterstützen, diese einzufordern. Zugleich setzt ombudschaftliche Beratung eine hohe fachliche Expertise voraus und ist selbst durch strukturelle Machtasymmetrie charakterisiert. Damit beinhaltet Ombudschaft gleichermaßen große Chancen, um jungen Menschen in der Kinder- und Jugendhilfe Gehör zu verschaffen – aber auch das Risiko den Machtüberhang zu nutzen und ohne ausreichende Partizipation stellvertretend für Ratsuchende zu handeln. Die bestehenden Asymmetrien müssen durch die Fachkräfte in Ombudsstellen gesehen, transparent verhandelt und aktiv ausgeglichen werden – durch verständliche Information, Aufklärung und Selbstbindung an die getroffenen Vereinbarungen. Im Beitrag werden die Möglichkeiten und Grenzen hierzu an einem Fallbeispiel verdeutlicht. Eine besondere Bedeutung erhält diese Auseinandersetzung auch vor dem Hintergrund der Ausweitung von Ombudsstellen nach § 9a SGB VIII und der Frage, wie in diesem Prozess der Anspruch von Ombudschaft zum verantwortungsvollen Umgang mit den gegebenen Strukturen aufrechterhalten werden kann und junge Menschen und ihre Familien Gehör finden können.