Community structure in networks naturally arises in various applications. But while the topic has received significant attention for static networks, the literature on community structure in temporally evolving networks is more scarce. In particular, there are currently no statistical methods available to test for the presence of community structure in a sequence of networks evolving over time. In this work, we propose a simple yet powerful test using e-values, an alternative to p-values that is more flexible in certain ways. Specifically, an e-value framework retains valid testing properties even after combining dependent information, a relevant feature in the context of testing temporal networks. We apply the proposed test to synthetic and real-world networks, demonstrating various features inherited from the e-value formulation and exposing some of the inherent difficulties of testing on temporal networks.

RUCAI is an AI-based open-source software platform designed for humanities and social sciences educational workflows that require traceability to assigned course materials. RUCAI has a teacher-only workspace in which teachers can curate class exercises grounded in curriculum documents (articles, book chapters, and notes). From here, the teacher can set up workspaces grounded explicitly in curriculum materials, learning outcomes, and course requirements for students to use throughout the course. The system is set up to run on institution-controlled infrastructure using FastAPI, PostgreSQL, and a local LLM model serving via Ollama, and is intended to be deployable on a GPU-enabled virtual machine. RUCAI’s primary contributions are practical: (1) it presents a software architecture for local-first, source-grounded educational AI that treats the course as the primary software object; (2) it documents concrete implementation choices for reliable ingestion and retrieval across heterogeneous course materials, including a three-tier extraction pipeline, word-based chunking, and intent-aware retrieval; and (3) it describes a publish-to-student mechanism that lets teachers freeze course context and prompt constraints into student-facing instances. The paper also reports pilot observations, current limitations, and future development directions.

Many social welfare measures will sometimes rank a large population of very well-faring people below a very large population of barely well-faring people. Thus, they entail the repugnant conclusion. If such rankings are indeed repugnant, there must be some mistake behind measures that imply them. This paper discusses what that mistake might be. After rejecting other proposals, I suggest that the mistaken assumption is that welfare values that are insignificant for individuals, due to being small, could have significant impact on social welfare. To avoid this mistake, two new measures are introduced that aggregate significant and insignificant welfare values differently.

This paper overviews an overlapping generations financial cash flow valuation model that evaluates the financial sustainability of English NHS Trusts. It quantifies the financial sustainability constraints related to societal demographic shifts that affect their ability to maintain service delivery quality. The financial model computes a new long-term financial sustainability performance metric based on the notion of the Social Return on Investment (“SROI”). The measure evaluates the financial sustainability of two English acute care hospital foundation Trusts. Significant generational imbalances are identified for both sample NHS Trusts, both within different cohorts of existing generations, and between existing and future generational cohorts. Suggestions are provided for implementing these ideas and expanding actuaries’ expertise and skill sets.

The integration of generative AI into humanities research presents both new opportunities and significant methodological challenges. While generative AI enables more sophisticated textual, visual, and historical analysis than ever before, it also introduces novel methodological risks. Prompts now function as measurement instruments, construct validity is entangled with model fluency, the increasing abstraction and subjectivity of tasks challenge single-answer evaluations, small changes in prompt or model configuration can materially alter substantive conclusions, and model training encodes strong theoretical assumptions prior to human interaction. This article advances a theory-first framework for generative-AI-assisted humanities research that traces how theoretical commitments intervene across the research lifecycle. Drawing on examples from the computational humanities and social sciences, it argues that theory operates as methodological infrastructure that constrains degrees of freedom, supports reproducibility, and enables principled iteration. By reframing generative AI as a site of theoretical inscription, the article outlines practical strategies for building robust and theoretically grounded humanities research in the era of generative AI.

The tail behavior of aggregates of heavy-tailed random vectors is known to be determined by the so-called principle of ‘one large jump’, be it for finite sums, random sums, or Lévy processes. We establish that, in fact, a more general principle is at play. Assuming that the random vectors are multivariate regularly varying on various subcones of the positive orthant $[0,\infty)^d$, first we show that their aggregates are also multivariate regularly varying on these subcones. This allows us to approximate certain tail probabilities rendered asymptotically negligible under classical regular variation. Second, we discover that depending on the structure of a particular tail event, the tail behavior of the aggregates may be characterized by more than a single large jump. Finally, we illustrate a similar phenomenon for regularly varying multivariate Lévy processes, establishing as well a relationship between regular variation of a multivariate Lévy process and multivariate regular variation of its Lévy measure on different subcones. The applicability of these results in financial and insurance risk management is discussed.

Trichuris muris infections in laboratory mice underpin current understanding of host-parasite interactions, yet the extent to which these insights apply to natural systems remains largely untested. In this study, we experimentally infected wild-caught Mastomys natalensis with Trichuris mastomysi under controlled conditions. Mice were assigned to either a single high-dose infection or repeated low-dose exposure. Larval infection success and intensity were assessed 21 days post-infection. Infection success was high in both exposure groups, and infection dose strongly influenced parasite burden, with high-dose animals carrying significantly more larvae than low-dose animals. These results demonstrate that T. mastomysi readily establishes in wild-caught M. natalensis and that exposure dose primarily affects early larval burden rather than infection success. By experimentally infecting a natural rodent host under controlled conditions, this study links mechanistic laboratory approaches with ecologically relevant host-parasite dynamics in the wild.

Introduction to the Special Issue ‘Trajectories of Democratic Innovations’.

As artificial intelligence (AI) systems rapidly develop in biomedical research, bioethical frameworks focused on human subjects research are often limited in their capacity to address ethical concerns related to data-driven and black-boxed AI algorithms. Calls to embed Ethical, Legal and Social Implications research into large research consortia focused on AI seek to address the challenges of applying models developed in the context of genomics research to AI projects. Foundational questions related to the conduct of such embedded ethics work include: what are the goals of this work, what counts as “ethics,” who qualifies as an ethics expert and what mechanisms, including organizational structures, can ensure success in achieving ethics goals alongside scientific goals by multidisciplinary research teams? We draw on empirical case studies (including semi-structured interviews and document analysis) of large, federally funded AI research projects in the US that required embedded ethics. Notable findings include the lack of consensus among key players regarding the definition of AI ethics and conflict over the scope of ethics work, which impedes the implementation of ethics-related goals in research practices. These conflicts highlight the differences in disciplinary cultures, contentious boundaries between technical and ethical expertise and the lack of power and impact of the ethics team. Our findings underscore that effective ethics integration depends on trust and respect for ethics scholarship through leadership and relationship-building. We argue that building long-lasting, equitable relationships requires institutional commitment through organizational frameworks and funding mechanisms that prioritize deliberation about ethical concerns, shared values and shared power, as well as co-developing ethics goals alongside science goals. A horizontal approach is needed to nurture joint relationships for fostering ethical and just biomedical AI research and development.