Inferring contact network characteristics from epidemic data via compact mean-field models

Modelling epidemics using contact networks provides a significant improvement over classical compartmental models by explicitly incorporating the network of contacts. However, while network-based models describe disease spread on a given contact structure, their potential for inferring the underlying network from epidemic data remains largely unexplored. In this work, we consider the edge-based compartmental model (EBCM), a compact and analytically tractable framework, and we integrate it within dynamical survival analysis (DSA) to infer key network properties along with parameters of the epidemic itself. Despite correlations between structural and epidemic parameters, our framework demonstrates robustness in accurately inferring contact network properties from synthetic epidemic simulations. Additionally, we apply the framework to real-world outbreaks—the 2001 UK foot-and-mouth disease outbreak and the COVID-19 epidemic in Seoul— to estimate both disease parameters and network characteristics. Our results show that our framework achieves good fits to real-world epidemic data and reliable short-term forecasts. These findings highlight the potential of network-based inference approaches to uncover hidden contact structures, providing insights that can inform the design of targeted interventions and public health strategies

The EU’s Digital Footprint: Shaping Data Governance in Japan and Singapore

The rapid development of the Internet and information and communication technologies (ICTs) over the past few decades has led to the emergence of a new digital order, attracting significant attention from both academia and policymakers. In the global digital domain, the European Union (EU) has assumed a distinctive role in shaping and influencing digital norms and standards. This status stems from the EU’s pioneering efforts, ranging from the Council of Europe’s Convention 108 (1981) to the more recent General Data Protection Regulation (GDPR), which have exerted far-reaching extraterritorial effects, influencing data laws and regulatory practices beyond the EU’s borders. However, there remains a lack of sufficient research on how these actors have progressively enacted and revised their data regulations in response to evolving EU standards. To address this gap, this article adopts a qualitative approach to examine how the EU’s evolving data regulations have diffused to and been adopted by two Asian countries – Japan and Singapore. By categorising diffusion mechanisms into incentive, socialisation, learning, competition, and emulation, this research further explores the operative mechanisms underpinning the diffusion process. This research argues that the EU’s diffuse-ability in Japan has demonstrated a gradual strengthening trend, with socialisation functioning as the primary mechanism driving this process. In contrast, the EU’s diffuse-ability in Singapore has remained relatively weak, with competition served as the dominant mechanism

Mapping the Proceedings: The Importance of Spatiality for Reconstructing Black and Multiracial Communities in Georgian London

Exploring the vast references to spatiality in eighteenth-century criminal justice records reveals the presence of Black and multiracial communities in Georgian London. Trial records remain arguably the most detailed source for exploring where, and with whom, ordinary Black people lived, worked, and socialised across the city. This article adapts Kenneth Little’s definition of a community, characterised ‘by a common background of experience’, from his study of race in post-war Cardiff to the context of eighteenth-century London. Communities in the Georgian metropole were formed from a variety of shared experiences, such as heritage dispossession, poverty, work and socialisation, as well as the forced necessity of sharing homes. Black people were part of many, often overlapping, communities of experience, rooted in a contextually heightened desire for friendship, protection and belonging. An understanding of the composition of these communities is integral for reconstructing how ordinary Black people experienced eighteenth-century London

A taxonomy of neuroscientific strategies based on interaction orders

In recent decades, neuroscience has advanced with increasingly sophisticated strategies for recording and analyzing brain activity, enabling detailed investigations into the roles of functional units, such as individual neurons, brain regions, and their interactions. Recently, new strategies for the investigation of cognitive functions regard the study of higher-order interactions— that is, the interactions involving more than two brain regions or neurons. While methods focusing on individual units and their interactions at various levels offer valuable and often complementary insights, each approach comes with its own set of limitations. In this context, a conceptual map to categorize and locate diverse strategies could be crucial to orient researchers and guide future research directions. To this end, we define the spectrum of orders of interaction, namely a framework that categorizes the interactions among neurons or brain regions based on the number of elements involved in these interactions. We use a simulation of a toy model and a few case studies to demonstrate the utility and the challenges of the exploration of the spectrum. We conclude by proposing future research directions aimed at enhancing our understanding of brain function and cognition through a more nuanced methodological framework

State, society, and market: Interpreting the norms and dynamics of China's AI governance

This study challenges the prevailing perception of China's AI governance as a monolithic, state-driven model and instead presents a nuanced analysis of its complex governance landscape. Utilizing governance theories, we develop an analytical framework examining key governing nodes, tools, actors, and norms. Through case studies on minor protection and content regulation, this study demonstrates that Chinese AI governance involves a diverse array of stakeholders—including the state, private sector, and society—who co-produce norms and regulatory mechanisms. Contrary to conventional narratives, China's governance approach adapts existing regulatory tools to meet new challenges, balancing political, social, and economic interests. This study highlights how China has rapidly formalized AI regulations, in areas such as minor protection and content regulation, setting a precedent in global AI governance. The findings contribute to a broader understanding of AI regulation beyond ideological binaries and offer insights relevant to international AI policy discussions

Assessing the Role of Honour Culture and Image Concerns in Impeding Apologies

Despite the known benefits of apologies, people often fail to apologize for wrongdoings. We examined the role of a cultural logic of honor—where apologizing may clash with concerns about maintaining an image of strength and toughness—in reluctance to apologize. Using general population samples from 14 societies in Mediterranean, East Asian, and Anglo-Western regions (N = 5,471), we explored links between honor values and norms, image concerns, and apology outcomes using multilevel mediation analyses. Members of groups with stronger honor endorsement reported stronger image concerns about apologizing relative to their concerns about not apologizing, which in turn predicted greater reluctance to apologize and fewer past apologies. However, groups with stronger honor endorsement did not show greater reluctance to apologize overall, and some individual-level facets of honor predicted better apology outcomes. Our results highlight the importance of considering honor as a multifaceted construct and including contextual factors and processes when studying reconciliation processes and obstacles to apologies

Entropy-based random models for hypergraphs

Network theory has primarily focused on pairwise relationships, disregarding many-body interactions: neglecting them, however, can lead to misleading representations of complex systems. Hypergraphs represent an increasingly popular alternative for describing polyadic interactions: our innovation lies in leveraging the representation of hypergraphs based on the incidence matrix for extending the entropy-based framework to higher-order structures. In analogy with the Exponential Random Graphs, we name the members of this novel class of models Exponential Random Hypergraphs. Here, we focus on two explicit examples, i.e. the generalisations of the Erd¨ os-R´enyi Model and of the Configuration Model. After discussing their asymptotic properties, we employ them to analyse real-world configurations: more specifically, i) we extend the definition of several network quantities to hypergraphs, ii) compute their expected value under each null model and iii) compare it with the empirical one, in order to detect deviations from random behaviours. Differently from currently available techniques, ours is analytically tractable, scalable and effective in singling out the structural patterns of real-world hypergraphs differing significantly from those emerging as a consequence of simpler, structural constraints

The role of Artificial Intelligence in the construction of news: Challenges and opportunities facing journalism in an age underlined by increasing distrust in knowledge-producing institutions.

News is constructed through a myriad of processes reflecting the cultural and social context in which newsrooms operate as well as the work routines and ownerships structures that govern news organisations. Natural language processing (NLP) and machine learning algorithms have now enabled news organisations to automate content creation, significantly improving efficiency. These algorithms can analyse data, generate headlines, and write news articles. Such innovations have opened opportunities for journalists to focus on investigative journalism and in-depth reporting, while also providing real-time news to an information-hungry audience. However, the rise of AI in news construction also brings its own set of challenges, one of the most significant issues being trust. This paper will discuss how AI is currently used in news organizations, highlighting successful projects and lessons learned. The democratisation of content creation and the potential for personalised, data-driven news experiences also hold immense promise. Yet the industry must grapple with profound issues of trust, ethics, and transparency to maintain the integrity of journalism in an era where traditional knowledge-producing institutions are met with scepticism

A Simplified Fish School Search Algorithm for Continuous Single Objective Optimisation

The Fish School Search (FSS) algorithm is a metaheuristic known for its distinctive exploration and exploitation operators and cumulative success representation approach. Despite its success across various problem domains, FSS presents issues due to its high number of parameters, making its performance susceptible to improper parameterisation. Additionally, the interplay between its operators requires a sequential execution in a specific order, requiring two fitness evaluations per iteration for each individual. This operator's intricacy and the number of fitness evaluations pose the issue of costly fitness functions and inhibit parallelisation. To address these challenges, this paper proposes a Simplified Fish School Search (SFSS) algorithm that preserves the core features of the original FSS while redesigning the fish movement operators and introducing a new turbulence mechanism to enhance population diversity and robustness against stagnation. The SFSS also reduces the number of fitness evaluations per iteration and minimises the algorithm's parameter set. Computational experiments were conducted using a benchmark suite from the CEC 2017 competition to compare the SFSS with the traditional FSS and five other well-known metaheuristics. The SFSS outperformed the FSS in 84\% of the problems, and achieved the best results among all algorithms in 10 of the 26 problems