Disclosing transition plans to meet future net zero climate targets requires organisations to fundamentally move beyond traditional historical-oriented stewardship reporting towards forward-looking accountability to meet their obligations to their future shareholders and stakeholders. However, despite a range of varying requirements concerning disclosure of climate-related targets to meet the Paris Agreement, confusion remains over the appropriate form, content and standard of transition plan disclosure that are required to implement these targets. The former UK based Transition Plan Taskforce set out globally leading requirements for transition plan reporting in 2023, however the extent to which these recommendations have since been implemented has not yet been comprehensively analysed. This paper summarises the key differences between UK, European and International guidelines for transition plans and then discusses the results of an analysis of variations in transition plan reporting practices by a sample of globally large financial and industrial organisations. It is predicted that a combination of both firm-level climate risk and country-level institutional factors are associated with the propensity to produce public transition plans. The empirical results are largely supportive of these predictions. Firms with greater levels of engagement with climate risk (as proxied by the CDP score), and UK and-or EU based firms, are more likely to produce climate transition plans. The empirical results are corroborated by qualitative analysis, which compares examples of good practice transition plan reporting by a sub-sample of firms within each industry sector. It is concluded that the resulting lack of clarity by regulatory authorities, and diversity in transition plan reporting practices by globally large financial and industrial firms, may potentially result in confusion and a lack of informed decision-making by their stakeholders and policymakers concerning climate-related resilience and risk mitigation actions.

Observed competitive market profit margins in property and casualty insurance have typically been higher than the capital assets pricing model adjustment for risky loss cashflows would suggest. Explanations for this difference include frictions from operating an insurance business and capital risks that are not adequately recognised and rewarded by the theory. It is proposed that the difference may instead be related to the consumption of insurance services and claim fulfilment with an additional fair profit margin evaluated using marginal utility pricing principles.

In many economies, youth unemployment rates over the past two decades have exceeded 10 percentage points, highlighting that not all youth successfully transition successfully from schooling to employment. Equally disturbing are the high rates of young adults not observed in employment, education, or training, a rate commonly referred to as “NEET.” There is not a single pathway for successful transitions. Understanding these pathways and the influences of geographic location, employment opportunities, and family and community characteristics that contribute to positive transitions is crucial. While abundant data exists to support this understanding, it is often siloed and not easily combined to inform schools, communities, and policymakers about effective strategies and necessary changes. Researchers prefer working with datasets, while many stakeholders favor results presented through storytelling and visualizations. This paper introduces YouthView, an innovative online platform designed to provide comprehensive insights into youth transition challenges and opportunities. YouthView integrates information from datasets on youth disadvantage indicators, employment, skills demand, and job vacancy at regional levels. The platform features two modes: a guided storytelling mode with selected visualizations, and an open-ended suite of exploratory dashboards for in-depth data analysis. This dual approach enables policymakers, community organizations, and education providers to gain a nuanced understanding of the challenges faced by different communities. By illuminating spatial patterns, socioeconomic disparities, and relationships between disadvantage factors and labor market dynamics, YouthView facilitates informed decision-making and the development of targeted interventions, ultimately contributing to improved youth economic outcomes and expanded opportunities in areas of greatest need.

A seminal result of Komlós, Sárközy, and Szemerédi states that any $n$-vertex graph $G$ with minimum degree at least $(1/2+\alpha )n$ contains every $n$-vertex tree $T$ of bounded degree. Recently, Pham, Sah, Sawhney, and Simkin extended this result to show that such graphs $G$ in fact support an optimally spread distribution on copies of a given $T$, which implies, using the recent breakthroughs on the Kahn-Kalai conjecture, the robustness result that $T$ is a subgraph of sparse random subgraphs of $G$ as well. Pham, Sah, Sawhney, and Simkin construct their optimally spread distribution by following closely the original proof of the Komlós-Sárközy-Szemerédi theorem which uses the blow-up lemma and the Szemerédi regularity lemma. We give an alternative, regularity-free construction that instead uses the Komlós-Sárközy-Szemerédi theorem (which has a regularity-free proof due to Kathapurkar and Montgomery) as a black box. Our proof is based on the simple and general insight that, if $G$ has linear minimum degree, almost all constant-sized subgraphs of $G$ inherit the same minimum degree condition that $G$ has.

We introduce a new family of coalescent mean-field interacting particle systems by producing a pinning property that acts over a chosen sequence of multiple time segments. Throughout their evolution, these stochastic particles converge in time (i.e. get pinned) to their random ensemble average at the termination point of any one of the given time segments, only to burst back into life and repeat the underlying principle of convergence in each of the successive time segments, until they are fully exhausted. Although the architecture is represented by a system of piecewise stochastic differential equations, we prove that the conditions generating the pinning property enable every particle to preserve their continuity over their entire lifetime almost surely. As the number of particles in the system increases asymptotically, the system decouples into mutually independent diffusions, which, albeit displaying progressively uncorrelated behaviour, still close in on, and recouple at, a deterministic value at each termination point. Finally, we provide additional analytics including a universality statement for our framework, a study of what we call adjourned coalescent mean-field interacting particles, a set of results on commutativity of double limits, and a proposal of what we call covariance waves.

We derive the exact asymptotics of $\mathbb{P} {\{\sup\nolimits_{\boldsymbol{t}\in {\mathcal{A}}}X(\boldsymbol{t})>u \}} \textrm{ as}\ u\to\infty,$ for a centered Gaussian field $X({\boldsymbol{t}}),\ {\boldsymbol{t}}\in \mathcal{A}\subset\mathbb{R}^n$, $n>1$ with continuous sample paths almost surely, for which $\arg \max_{\boldsymbol{t}\in {\mathcal{A}}} {\mathrm{Var}}(X(\boldsymbol{t}))$ is a Jordan set with a finite and positive Lebesgue measure of dimension $k\le n$ and its dependence structure is not necessarily locally stationary. Our findings are applied to derive the asymptotics of tail probabilities related to performance tables and chi processes, particularly when the covariance structure is not locally stationary.

With the growing amount of historical infrastructure data available to engineers, data-driven techniques have been increasingly employed to forecast infrastructure performance. In addition to algorithm selection, data preprocessing strategies for machine learning implementations plays an equally important role in ensuring accuracy and reliability. The present study focuses on pavement infrastructure and identifies four categories of strategies to preprocess data for training machine-learning-based forecasting models. The Long-Term Pavement Performance (LTPP) dataset is employed to benchmark these categories. Employing random forest as the machine learning algorithm, the comparative study examines the impact of data preprocessing strategies, the volume of historical data, and forecast horizon on the accuracy and reliability of performance forecasts. The strengths and limitations of each implementation strategy are summarized. Multiple pavement performance indicators are also analysed to assess the generalizability of the findings. Based on the results, several findings and recommendations are provided for short-to medium-term infrastructure management and decision-making: (i) in data-scarce scenarios, strategies that incorporate both explanatory variables and historical performance data provides better accuracy and reliability, (ii) to achieve accurate forecasts, the volume of historical data should at least span a time duration comparable to the intended forecast horizon, and (iii) for International Roughness Index and transverse crack length, a forecast horizon up to 5 years is generally achievable, but forecasts beyond a three-year horizon are not recommended for longitudinal crack length. These quantitative guidelines ultimately support more effective and reliable application of data-driven techniques in infrastructure performance forecasting.

Community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) is a significant public health concern, disproportionately affecting socioeconomically disadvantaged populations, including individuals experiencing poverty, homelessness, incarceration, and injection drug use. This scoping review synthesizes existing literature on factors influencing CA-MRSA occurrence and community transmission in these populations. A comprehensive search of PubMed, MEDLINE, and Scopus for studies published between January 2000 and February 2024 identified 3,223 articles, of which 40 met the inclusion criteria. Findings indicate that the CA-MRSA burden remains high, with community transmission influenced by factors, such as limited access to hygiene resources, structural barriers to care, and social network dynamics. Surveillance and intervention strategies remain largely healthcare-focused, with limited data on community-level transmission and risk. This review highlights the urgent need for targeted public health interventions and the adoption of expanded, innovative surveillance methods, such as genomic epidemiology, to better track and mitigate CA-MRSA transmission in vulnerable populations. As antibiotic resistance continues to rise, future research should prioritize longitudinal studies and community-based surveillance to develop effective, population-specific infection prevention, and control strategies.

Here we consider the hypergraph Turán problem in uniformly dense hypergraphs as was suggested by Erdős and Sós. Given a $3$-graph $F$, the uniform Turán density $\pi _{\boldsymbol{\therefore }}(F)$ of $F$ is defined as the supremum over all $d\in [0,1]$ for which there is an $F$-free uniformly $d$-dense $3$-graph, where uniformly $d$-dense means that every linearly sized subhypergraph has density at least $d$. Recently, Glebov, Král’, and Volec and, independently, Reiher, Rödl, and Schacht proved that $\pi _{\boldsymbol{\therefore }}(K_4^{(3)-})=\frac {1}{4}$, solving a conjecture by Erdős and Sós. Despite substantial attention, the uniform Turán density is still only known for very few hypergraphs. In particular, the problem due to Erdős and Sós to determine $\pi _{\boldsymbol{\therefore }}(K_4^{(3)})$ remains wide open.

In this work, we determine the uniform Turán density of the $3$-graph on five vertices that is obtained from $K_4^{(3)-}$ by adding an additional vertex whose link forms a matching on the vertices of $K_4^{(3)-}$. Further, we point to two natural intermediate problems on the way to determining $\pi _{\boldsymbol{\therefore }}(K_4^{(3)})$, and solve the first of these.

This study assesses the seroprevalence of Rift Valley fever (RVF) in ruminants in Dhobley, Somalia, following a 2021 outbreak in Kenya. Among 142 ruminants sampled, 4.9% were seropositive for RVF virus (RVFV) antibody, with IgM antibodies (1.4%) indicating recent exposure, though no cases were RT-PCR-positive. Unregulated livestock movement and limited surveillance pose significant risks for future outbreaks, underscoring the need for enhanced surveillance systems and One Health strategies.

In this paper we study one-sided hypothesis testing under random sampling without replacement, which frequently appears in the cryptographic problem setting, including the verification of measurement-based quantum computation. Suppose that $n+1$ binary random variables $X_1,\ldots, X_{n+1}$ follow a permutation invariant distribution and n binary random variables $X_1,\ldots, X_{n}$ are observed. Then, we propose randomized tests with a randomization parameter for the expectation of the $(n+1)$th random variable $X_{n+1}$ under a given significance level $\delta>0$. Our randomized tests significantly improve the upper confidence limit over deterministic tests. Our problem setting commonly appears in machine learning in addition to cryptographic scenarios by considering adversarial examples. Such studies are essential for expanding the applicable area of statistics. Although this paper addresses only binary random variables, a similar significant improvement by randomized tests can be expected for general non-binary random variables.

We prove that for every locally stable and tempered pair potential $\phi$ with bounded range, there exists a unique infinite-volume Gibbs point process on $\mathbb{R}^{d}$ for every activity $\lambda < ({e}^{L} \hat{C}_{\phi})^{-1}$, where L is the local stability constant and $\hat{C}_{\phi} \,:\!=\, \sup_{x \in \mathbb{R}^{d}} \int_{\mathbb{R}^{d}} 1 - {e}^{-\left\lvert \phi(x, y) \right\rvert} \mathrm{d} y$ is the (weak) temperedness constant. Our result extends the uniqueness regime that is given by the classical Ruelle–Penrose bound by a factor of at least ${e}$, where the improvements become larger as the negative parts of the potential become more prominent (i.e. for attractive interactions at low temperature). Our technique is based on the approach of Dyer et al. (2004 Random Structures & Algorithms 24, 461–479): We show that for any bounded region and any boundary condition, we can construct a Markov process (in our case spatial birth–death dynamics) that converges rapidly to the finite-volume Gibbs point process while the effects of the boundary condition propagate sufficiently slowly. As a result, we obtain a spatial mixing property that implies uniqueness of the infinite-volume Gibbs measure.

In this paper we consider a dynamic Erdős–Rényi graph in which edges, according to an alternating renewal process, change from present to absent and vice versa. The objective is to estimate the on- and off-time distributions while only observing the aggregate number of edges. This inverse problem is dealt with, in a parametric context, by setting up an estimator based on the method of moments. We provide conditions under which the estimator is asymptotically normal, and we point out how the corresponding covariance matrix can be identified. We also demonstrate how to adapt the estimation procedure if alternative subgraph counts are observed, such as the number of wedges or triangles.

This paper considers two supercritical branching processes with immigration in different random environments, denoted by $\{Z_{1,n}\}$ and $\{Z_{2,m}\}$, with criticality parameters µ1 and µ2, respectively. Under certain conditions, it is known that $\frac{1}{n} \log Z_{1,n} \to \mu_1$ and $\frac{1}{m} \log Z_{2,m} \to \mu_2$ converge in probability as $m, n \to \infty$. We present basic properties about a central limit theorem, a non-uniform Berry–Esseen’s bound, and Cramér’s moderate deviations for $\frac{1}{n} \log Z_{1,n} - \frac{1}{m} \log Z_{2,m}$ as $m, n \to \infty$. To this end, applications to construction of confidence intervals and simulations are also given.

Let $K^r_n$ be the complete $r$-uniform hypergraph on $n$ vertices, that is, the hypergraph whose vertex set is $[n] \, :\! = \{1,2,\ldots ,n\}$ and whose edge set is $\binom {[n]}{r}$. We form $G^r(n,p)$ by retaining each edge of $K^r_n$ independently with probability $p$. An $r$-uniform hypergraph $H\subseteq G$ is $F$-saturated if $H$ does not contain any copy of $F$, but any missing edge of $H$ in $G$ creates a copy of $F$. Furthermore, we say that $H$ is weakly $F$-saturated in $G$ if $H$ does not contain any copy of $F$, but the missing edges of $H$ in $G$ can be added back one-by-one, in some order, such that every edge creates a new copy of $F$. The smallest number of edges in an $F$-saturated hypergraph in $G$ is denoted by ${\textit {sat}}(G,F)$, and in a weakly $F$-saturated hypergraph in $G$ by $\mathop {\mbox{$w$-${sat}$}}\! (G,F)$. In 2017, Korándi and Sudakov initiated the study of saturation in random graphs, showing that for constant $p$, with high probability ${\textit {sat}}(G(n,p),K_s)=(1+o(1))n\log _{\frac {1}{1-p}}n$, and $\mathop {\mbox{$w$-${sat}$}}\! (G(n,p),K_s)=\mathop {\mbox{$w$-${sat}$}}\! (K_n,K_s)$. Generalising their results, in this paper, we solve the saturation problem for random hypergraphs $G^r(n,p)$ for cliques $K_s^r$, for every $2\le r \lt s$ and constant $p$.

In England, Shiga toxin-producing Escherichia coli (STEC) serogroup O26 has recently emerged as a public health concern, despite fewer than half of diagnostic laboratories in England having the capability to detect non-O157 STEC. STEC O26 cases frequently report exposure to farms or nurseries. We describe the epidemiology of STEC O26 and examine evidence for a relationship between O26 and exposure to these settings. We analysed national surveillance data describing laboratory-confirmed STEC cases and public health incidents over the past 10 years to explore the incidence, clinical outcomes, and association with farms and nurseries for STEC O26 cases compared to STEC O157 and other serogroups. Between 2014 and 2023, the proportion of STEC notifications which were STEC O26 increased from 2% (19/956) to 12% (234/1946). After adjusting for age, we found no difference in the likelihood of farm or nursery attendance between O26 and O157 cases but a significantly higher risk of HUS in O26 (adjusted risk ratio 3.13 (2.18–4.51)). We demonstrate that STEC O26 is associated with the same risk of farm or nursery attendance as other STEC serogroups but a higher risk of severe morbidity. Our findings reinforce the need for improved surveillance of non-O157 STEC.