Simulating turbulent fluid flows is a computationally prohibitive task, as it requires the resolution of fine-scale structures and the capture of complex nonlinear interactions across multiple scales. Consequently, extensive research has focused on analysing turbulent flows from a data-driven perspective. However, due to the complex and chaotic nature of these systems, traditional models often become unstable. To overcome these limitations, we propose a purely stochastic approach that separately addresses the evolution of large-scale coherent structures and the closure of high-fidelity statistical data. To this end, the dynamics of the filtered data are learnt using an autoregressive model. This combines a variational-autoencoder (VAE) and Transformer architecture. The VAE projection is probabilistic, ensuring consistency between the model’s stochasticity and the flow’s statistical properties. The mean realisation of stochastically sampled trajectories from our model shows relative $ {L}_1 $ and $ {L}_2 $ distances of 6% and 10%, respectively. Moreover, our framework enables the construction of meaningful confidence intervals, achieving a prediction interval coverage probability of 80% with minimal interval width. To recover high-fidelity velocity fields from the filtered space, Gaussian Process (GP) regression is employed. This strategy has been tested in the context of a Kolmogorov flow exhibiting chaotic behavior. We compare the performance of our model with state-of-the-art probabilistic baselines, including a VAE and a diffusion model. We demonstrate that our Gaussian process-based closure outperforms these baselines in capturing first and second moment statistics in this particular test bed, providing robust and adaptive confidence intervals.

Aligned with the increasing trend observed across the EU/EEA, congenital syphilis (CS) cases have risen in Portugal, which has the third highest rate per 100000 live births in the EU/EEA. This study aimed to analyse CS cases reported in Portugal, focusing on pregnancy monitoring and antenatal screening to identify gaps in preventing vertical transmission of syphilis. We conducted a descriptive study, including confirmed CS cases reported in Portugal from 2015 to 2024. We calculated annual incidence per 100000 live births and the proportion of pregnancies monitored and antenatal screenings performed. During 2015–2024, 99 confirmed CS cases were reported, 64.6% in infants under 1 month of age. The incidence of CS increased eightfold from 2016 to 2024. Among mothers of CS cases, 67.7% had pregnancies classified as monitored; of these, 77.6% had a record of antenatal screening, and 88.5% of those screened tested positive. These findings highlight potential fragilities in antenatal care, diagnosis and treatment, contributing to the resurgence of CS in Portugal. Addressing missed opportunities for prevention requires improving maternal healthcare, strengthening surveillance systems, and ensuring the timely treatment of pregnant people and their partners, in order to reverse this trend and move towards the elimination of vertical transmission of syphilis.

We study the activated random walk model on the one-dimensional ring, in the high-density regime. We develop a toppling procedure that gradually builds an environment that can be used to show that activity will be sustained for a long time. This yields a self-contained and relatively short proof of existence of a slow phase for arbitrarily large sleep rates.

In September 2020, an unexpected increase in Salmonella Muenchen patient isolates and notifications was observed. We investigated the outbreak to identify the vehicle of infection. RKI defined cases as patients with laboratory-confirmed S. Muenchen infections reported between September 2020 and July 2021. Genomes of clinical, food, and animal S. Muenchen isolates were analysed using cgMLST. We conducted interviews and performed a frequency-matched case–control study. We calculated frequencies and adjusted odds ratios (aOR) using logistic regression. We identified 301 cases in eight federal states in Germany. Hypothesis-generating interviews did not provide a conclusive hint of a possible vehicle. S. Muenchen strains were detected in dried coconut pieces, milk powder used for chocolate production, and a wild swan, all with a cgMLST profile indistinguishable from the prominent node comprising 116 patient isolates. Cases included in the case–control study more often consumed dried coconut pieces (22/30) than controls (2/116) (aOR: 176 (95% confidence interval: 32–954)). In this investigation, cgMLST analysis presented identical strains in three different isolate sources. The case–control study supported dried coconut pieces as vehicle of infection demonstrating the importance of interdisciplinary investigations and underscoring the potential impact of unusual vehicles.

As a major metropolitan city, London faces persistent road congestion and severe air pollution. To address these issues, static electronic road pricing (ERP) models have been implemented. While effective, these are inherently limited in flexibility. This paper explores dynamic ERP models to improve upon static pricing by minimizing air pollution and traffic congestion within the Congestion Charge Zone. The problem is formulated as a multi-stakeholder multi-objective optimization problem, incorporating the perspectives of three stakeholders—the government, vehicle owners, and environmental organizations—and three objectives: air pollution, traffic congestion, and price. The NSGA-II optimization algorithm was applied on a representative day and demonstrated substantial improvements. The concentration of PM$ {}_{2.5} $—the more harmful pollutant—was reduced by up to 23%, while NO2 levels fell by 2–3%. Traffic flow, used as a proxy for congestion, decreased by approximately 3–4% during peak hours. These improvements were achieved with only a modest increase in the mean price to £12.51 (from a baseline of £11.50), with a standard deviation of £1.59 and a variance of £2.43 across hourly prices. These results suggest that targeted dynamic pricing—when aligned with environmental and behavioural incentives—can deliver measurable gains in urban air quality and congestion without imposing a significant cost burden on drivers. A core novelty of this work lies in its practical, stakeholder-inclusive problem formulation. While the approach assumes infrastructure for automated price deduction and routing, this limitation can be addressed in future work through advances in vehicle–infrastructure communication systems.

We investigate a novel first-passage percolation model, referred to as the Brochette first-passage percolation model, where the passage times associated with edges lying on the same line are equal. First, we establish a point-to-point convergence theorem, identifying the time constant. In particular, we explore the case where the time constant vanishes and demonstrate the existence of a wide range of possible behaviours. Next, we prove a shape theorem, showing that the limiting shape is the $L^1$ diamond. Finally, we extend the analysis by proving a point-to-point convergence theorem in the setting where passage times are allowed to be infinite.

The Nelson–Siegel model is widely used in fixed income markets to produce yield curve dynamics. The multiple time-dependent parameter model conveniently addresses the level, slope, and curvature dynamics of the yield curves. In this study, we present a novel state-space functional regression model that incorporates a dynamic Nelson–Siegel (DNS) model and functional regression formulations applied to a multi-economy setting. This framework offers distinct advantages in explaining the relative spreads in yields between a reference economy and a response economy. To address the inherent challenges of model calibration, a kernel principal component analysis is employed to transform the representation of functional regression into a finite-dimensional, tractable estimation problem. A comprehensive empirical analysis is conducted to assess the efficacy of the functional regression approach, including an in-sample performance comparison with the DNS model. We conducted the stress testing analysis of the yield curves’ term structure within a dual economy framework. The bond ladder portfolio was examined through a case study focused on spread modeling using historical data for US Treasury and UK bonds.

We study off-diagonal Ramsey numbers $r(H, K_n^{(k)})$ of $k$-uniform hypergraphs, where $H$ is a fixed linear $k$-uniform hypergraph and $K_n^{(k)}$ is complete on $n$ vertices. Recently, Conlon, Fox, Gunby, He, Mubayi, Suk, and Verstraëte disproved the folklore conjecture that $r(H, K_n^{(3)})$ always grows polynomially in $n$. In this paper, we show that much larger growth rates are possible in higher uniformity. In uniformity $k\ge 4$, we prove that for any constant $C\gt 0$, there exists a linear $k$-uniform hypergraph $H$ for which

\begin{equation*} r(H,K_n^{(k)}) \geq {\textrm {twr}}_{k-2}(2^{(\log n)^C}). \end{equation*}

In spite of the omnibus property of integrated conditional moment (ICM) specification tests, they are not commonly used in empirical practice owing to features such as the non-pivotality of the test and the high computational cost of available bootstrap schemes, especially in large samples. This article proposes specification and mean independence tests based on ICM metrics. The proposed test exhibits consistency, asymptotic $\chi ^2$-distribution under the null hypothesis, and computational efficiency. Moreover, it demonstrates robustness to heteroskedasticity of unknown form and can be adapted to enhance power toward specific alternatives. A power comparison with classical bootstrap-based ICM tests using Bahadur slopes is also provided. Monte Carlo simulations are conducted to showcase the excellent size control and competitive power of the proposed test.

Understanding the values held by negotiating parties is central to the design and success of international climate change agreements. However, empirical understandings of these values – and the manners by which they structure negotiating countries’ value networks and interactions over time – are severely limited. In addressing this shortcoming, this paper uses keyword-assisted topic models to extract value networks for the 13 most recent Conferences of the Parties (COPs) to the United Nations Framework Convention on Climate Change (UNFCCC). It then uses network analysis tools to unpack these networks in relation to influential values, countries, and time. In doing so, it demonstrates that countries’ core climate change values (i) can be accurately recovered from COP High-level Segment (HLS) speeches and (ii) can, in turn, be used to understand the structure of negotiation networks at the UNFCCC. Analysis of the corresponding value networks for COPs 16–28 indicates that initially central values of “Fairness” and “Power” have increasingly given way to values associated with the “Environment” and “Achievement.” Thus, countries at the UNFCCC have increasingly eschewed values associated with common but differentiated responsibilities in favor of a consensus over the urgency of collectively combating climate change. These and related insights illustrate our approach’s potential for recovering and understanding value networks within climate change negotiations – a critical first step for any successful climate change agreement.

The role of data and automated (non-artificial intelligence [AI]) algorithmic targeting in adaptive social cash systems is gaining increasing significance, but few governments have yet leveraged on AI technologies to reap its benefits. Hence, there is mounting pressure on social cash policymakers and practitioners to rapidly embrace the opportunities arising from AI applications, especially in times of crisis. While data and algorithmic targeting (non-AI and AI) are efficient in enrolling beneficiaries in emergency social cash systems, it may also pose serious challenges. Through a qualitative case study of an adaptive social cash programme in Pakistan, the research critically examines the data/algorithmic targeting process, and unveils the shortcomings prevalent in design, data and algorithmic decision-making that lead to certain exclusionary outcomes. The study makes several contributions to the data and policy literature. Drawing on the limitations, it first offers a set of practical recommendations for greater enrolment, and hence inclusion of beneficiaries. Second, it discusses novel opportunities that AI technologies may present in adaptive social cash systems, whilst carefully assessing the risks. Third, the study proposes an organisational AI governance framework to guide the development of responsible and ethical AI practices. The study affords policy and practical implications for governments, social cash policymakers, and practitioners in providing invaluable insights into how changing targeting practices, via AI technologies, under a governance framework can direct ethical practices that positively impacts on beneficiaries, social cash organisations, and stakeholders.

On 17 October 2023, the County of San Diego (CoSD), California’s Epidemiology and Immunization Services Branch received reports of eight salmonellosis patients with illness onsets beginning in September 2023. CoSD disease investigators identified common exposure to Farm A-produced unpasteurized dairy products and performed epidemiologic and environmental investigations. Isolates were submitted for whole-genome sequencing. Cases were defined as persons with Salmonella infection with an isolation date on or after 15 September 2023. Twenty-five cases, including 23 confirmed and two probable, were identified among county residents, with 19 (76%) reporting exposure to Farm A unpasteurized dairy products. Median patient age was 12 years (range: 1–47 years). Three children (12%) were hospitalized. Environmental samples of unpasteurized milk collected from Farm A tested positive for S. Typhimurium, matching the outbreak strain by WGS. After Salmonella identification, CoSD released early advisories warning of Farm A-associated risks, likely preventing additional infections.

We study a family of Crump–Mode–Jagers branching processes in a random environment that explode, i.e. that grow infinitely large in finite time with positive probability. Building on recent work of Iyer and the author (‘On the structure of genealogical trees associated with explosive Crump–Mode–Jagers branching processes’, arXiv:2311.14664, 2023), we weaken certain assumptions required to prove that the branching process, at the time of explosion, contains a (unique) individual with infinite offspring. We then apply these results to super-linear preferential attachment models. In particular, we fill gaps in some of the cases analysed in Appendix A of the work of Iyer and the author and study a large range of previously unattainable cases.

Genomic epidemiology was essential for characterizing SARS-CoV-2 transmission during the early COVID-19 pandemic. This systematic review examined how whole-genome sequencing was used in local outbreak investigations published between March 2020 and March 2021. Searches of PubMed, Scopus, and Web of Science identified 32 studies from 18 countries that integrated genomic and epidemiological data for local outbreak investigations. Most studies were conducted in healthcare settings or in high-income countries. A limited number of studies were conducted in low- and middle-income countries, except for China and Vietnam. Illumina or Oxford Nanopore platforms and tiled-amplicon protocols were the most common sequencing methods. Phylogenetic trees were the most common genomic epidemiology analytical approach. Genomic data enabled confirmation of suspected transmission links, detection of multiple introductions, and identification of asymptomatic or presymptomatic transmission. Important enablers of early implementation included open-access genomics databases, standardized protocols (e.g. ARTIC), open-source tools (e.g. Nextstrain), and cross-sector partnerships and funding. Study quality and adherence to common observational study reporting guidelines varied widely. Familiarity with the STROME-ID guidelines for molecular epidemiology studies would have improved overall quality. These findings highlight the utility of genomic epidemiology in outbreak response and support its continued integration into public health surveillance systems.

In this paper we derive identities for the upward and downward exit problems and resolvents for a process whose motion changes between two Lévy processes if it is above (or below) a barrier b and coincides with a Poissonian arrival time. This can be expressed in the form of a (hybrid) stochastic differential equation, for which the existence of its solution is also discussed. All identities are given in terms of new generalisations of scale functions (counterparts of the scale functions from the theory of Lévy processes). To illustrate the applicability of our results, the probability of ruin is obtained for a risk process with delays in the dividend payments.

We study the number of triangles $T_n$ in the sparse $\beta$-model on n vertices, a random graph model that captures degree heterogeneity in real-world networks. Using the norms of the heterogeneity parameter vector, we first determine the asymptotic mean and variance of $T_n$. Next, by applying the Malliavin–Stein method, we derive a non-asymptotic upper bound on the Kolmogorov distance between the normalized $T_n$ and the standard normal distribution. Under an additional assumption on degree heterogeneity, we further prove the asymptotic normality for $T_n$ as $n\to\infty$.

Failure extropy, introduced by Nair and Sathar Nair [(2020). On dynamic failure extropy. J. Indian Soc. Probab. Stat. 21: 287–-313], provides a complementary perspective to entropy for quantifying uncertainty in lifetime distributions. However, it becomes mathematically invalid for distributions with unbounded support. To overcome this limitation, Tahmasebi and Toomaj [(2022). On negative cumulative extropy with applications. Commun. Stat. Theory Methods 51(15): 5025-–5047] proposed the concept of negative cumulative extropy (NCEx), offering a bounded and interpretable alternative. In this paper, we extend the notion of NCEx to the bivariate dynamic setting, where uncertainty is assessed for systems whose components have failed at specified times. The proposed formulation effectively captures the uncertainty associated with past lifetimes under dependence, which the existing NCEx cannot address. The measure is further generalized to a vector-valued form, and its fundamental properties are established, including monotonicity, invariance, bounds expressed in terms of the expected inactivity time, and key characterizations. A new stochastic ordering based on the proposed measure is also established. To facilitate practical implementation, a nonparametric estimator is developed and its performance evaluated through extensive Monte Carlo simulations. The practical relevance of the proposed measure is demonstrated using a real dataset, and its superiority over existing entropy-based approaches is shown on an additional dataset.