This work studies time averages of an observable $h(t,X_t)$, where $X_t$ is the solution to a time-inhomogeneous stochastic differential equation (SDE) driven by drift, b(t, x), and diffusion, $\sigma(t{,}{\kern.5pt}x)$, that change sufficiently slowly in time. In this quasistatic regime we derive an approximation to the time average that is computable from properties of the time-homogeneous SDEs driven by $b(t,\cdot)$ and $\sigma(t,\cdot)$ with fixed t; specifically, we utilize $\log$-Sobolev inequalities for the instantaneous invariant distribution and generator for each t. We obtain explicit non-asymptotic error bounds on this quasistatic approximation, both in the form of concentration inequalities and bounds on the expected value. The error bounds demonstrate a competition between the speed of convergence to the instantaneous invariant distributions and their rate of change, matching the intuition that underlies the quasistatic approximation.

We propose a high-dimensional extension of the heteroscedasticity test proposed in Newey and Powell (1987). Our test is based on expectile regression in the proportional asymptotic regime where $n/p \to \delta \in (0,1]$. The asymptotic analysis of the test statistic uses the approximate message passing algorithm, from which we obtain the limiting distribution of the test and establish its asymptotic power. The numerical performance of the test is validated through an extensive simulation study. As real-data applications, we present the analysis based on “international economic growth” data (Belloni et al., 2013), which is found to be homoscedastic, and “supermarket” data (Lan et al., 2016), which is found to be heteroscedastic.

This paper focuses mainly on the Euler scheme of stochastic delay differential equations with locally Lipschitz coefficients. The convergence in probability of the Euler scheme and the corresponding weak limit process of the normalized error process are derived. Furthermore, this paper also considers a class of specific degenerate stochastic delay equations and obtains the associated weak limit process for the stronger error process. The error parameter of this stronger error process for such a degenerate system is n instead of $\sqrt{n}$ in the normalized error process. This causes substantial challenges in the analysis and proofs and the weak limit process also becomes more complicated and involves some additional terms. This result is new and interesting even for the non-delay case.

Greenwashing poses a significant challenge to the fight against climate change by undermining trust in corporate sustainability claims. This study introduced the greenwashing tendency score (GTS), an automatable method designed to detect greenwashing tendencies in corporate sustainability reports. By leveraging textual sentiment and alignment analysis techniques in conjunction with environmental, social, and governance ratings, the GTS quantifies discrepancies between communicated and actual sustainability performance. We applied our methodology to 36 German stock index companies during the years from 2020 to 2022. Our key findings reveal substantial variations in greenwashing tendencies among these companies, emphasizing the need for more transparent and reliable sustainability reporting. The GTS emerged as a scalable, reproducible, and objective tool that can aid, for example, investors, regulators, and Non-government organizations in identifying greenwashing practices. This research contributed to the sustainable finance literature by introducing a neutral and open measure to assess firms’ greenwashing tendency, summarizing implications for policymaking and regulatory authorities and discussing its potential for long-term accountability and integrity in corporate sustainability communications.

Enterovirus A71 was first isolated in California in 1969, with the earliest retrospective detection traced back to 1963 in the Netherlands, but its early spread remains unclear. Using age-specific seroprevalence data from children aged 1–10 years in Kawasaki City, Japan, collected annually from 1966–1973, we applied serocatalytic models to estimate annual force of infection during 1959–1973. Several models were tested, incorporating different assumptions about time-varying force of infection, age-dependent susceptibility, and seroreversion, to identify the best fit to the data. Model comparison identified the models with independent annual infection probability or two distinct outbreak periods, both including age-dependent force of infection and seroreversion, as optimal. All top models consistently identified two major transmission periods: 1961–1962 and 1968–1969. The two-outbreak model estimated mean attack rates of 21.8% and 37.8% for the earlier and later outbreaks under seroreversion, and 19.8% and 34.9% under age-dependent force of infection. Our findings provide evidence of enterovirus A71 circulation in Japan during two distinct periods in the 1960s, coinciding with early detections in Europe and the USA, suggesting global distribution by that decade. This study underscores the value of testing archived sera for reconstructing pathogen emergence and spread.

In classical credibility theory, estimation is typically limited to the hypothetical mean, restricting its use for premium principles that depend on higher-order moments. To address this, we develop a credibility-based framework for estimating the process variance under both known and unknown hypothetical means and apply these estimators to a broad class of variance-related premium principles, including the expected value, variance, standard deviation, and modified-variance principles. The estimators are derived via constrained linear projection techniques, minimizing the mean squared error between the estimator and the true process variance. Explicit formulas are obtained that are optimal among affine transformations of the data. The proposed estimators exhibit desirable statistical properties, including conditional unbiasedness, consistency, mean squared error convergence, and asymptotic normality. Numerical studies demonstrate their favorable convergence behavior, and an empirical analysis with real insurance data highlights their practical relevance. This framework extends Bühlmann’s classical credibility theory to second-moment estimation while remaining computationally tractable and requiring only mild moment conditions, without specifying the population or prior distributions.

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.

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.