This paper provides practical guidance to UK-based financial institutions (UKFIs) that are subject to the “operational resilience” guideline requirements of the Bank of England (BoE), Prudential Regulatory Authority and Financial Conduct Authority, issued in 2021, and fully effective for 31 March 2025. It contains practical suggestions and recommendations to assist UKFIs in implementing the guidelines. The scope of the paper covers issues related to (a) overviewing the latest equivalent operational resilience guidance in other countries and internationally (b) identifying key issues related to risk culture, risk appetite, information technology, tolerance setting, risk modelling, scenario planning and customer oriented operational resilience (c) identifying a framework for operational resilience based on a thorough understanding of these parameters and (d) designing and implementing an operational resilience maturity dashboard based on a sample of large UKIFs. The study also contains recommendations for further action, including enhanced controls and operational risk management frameworks. It concludes by identifying imperative policy actions to ensure that the implementation of the guidelines is more effective.

In this paper we adopt the probabilistic mean value theorem in order to study differences of the variances of transformed and stochastically ordered random variables, based on a suitable extension of the equilibrium operator. We also develop a rigorous approach aimed at expressing the variance of transformed random variables. This is based on a joint distribution which, in turn, involves the variance of the original random variable, as well as its mean residual lifetime and mean inactivity time. Then we provide applications to the additive hazards model and to some well-known random variables of interest in actuarial science. These deal with a new notion, called the ‘centred mean residual lifetime’, and a suitably related stochastic order. Finally, we also address the analysis of the differences of the variances of transformed discrete random variables thanks to the use of a discrete version of the equilibrium operator.

The gambler’s ruin problem for correlated random walks (CRWs), both with and without delays, is addressed using the optional stopping theorem for martingales. We derive closed-form expressions for the ruin probabilities and the expected game duration for CRWs with increments $\{1,-1\}$ and for symmetric CRWs with increments $\{1,0,-1\}$ (CRWs with delays). Additionally, a martingale technique is developed for general CRWs with delays. The gambler’s ruin probability for a game involving bets on two arbitrary patterns is also examined.

Cryptosporidium parvum is a well-established cause of gastrointestinal illness in both humans and animals and often causes outbreaks at animal contact events, despite the availability of a code of practice that provides guidance on the safe management of these events. We describe a large C. parvum outbreak following a lamb-feeding event at a commercial farm in Wales in 2024, alongside findings from a cohort study to identify high-risk exposures. Sixty-seven cases were identified, 57 were laboratory-confirmed C. parvum, with similar genotypes. Environmental investigations found a lack of adherence to established guidance. The cohort study identified 168 individuals with cryptosporidiosis-like illness from 540 exposure questionnaires (distributed via email to 790 lead bookers). Cases were more likely to have had closer contact with lambs (odds ratio (OR) kissed lambs = 2.4, 95% confidence interval (95% CI): 1.2–4.8). A multivariable analysis found cases were more likely to be under 10 years (adjusted OR (aOR) = 4.5, 95% CI: 2.0–10.0) and have had visible faeces on their person (aOR = 3.6, 95% CI: 2.1–6.2). We provide evidence that close contact at lamb-feeding events presents an increased likelihood of illness, suggesting that farms should limit animal contact at these events and that revisions to established codes of practice may be necessary. Enhancing risk awareness among farmers and visitors is needed, particularly regarding children.

In this paper we study the optimal multiple stopping problem with weak regularity for the reward, where the reward is given by a set of random variables indexed by stopping times. When the reward family is upper semicontinuous in expectation along stopping times, we construct the optimal multiple stopping strategy using the auxiliary optimal single stopping problems. We also obtain the corresponding results when the reward is given by a progressively measurable process.

This article aims at facilitating the widespread application of Energy Management Systems (EMSs), especially in buildings and cities, in order to support the realization of future carbon-neutral energy systems. We claim that economic viability is a severe issue for the utilization of EMSs at scale and that the provisioning of forecasting and optimization algorithms as a service can make a major contribution to achieving it. To this end, we present the Energy Service Generics software framework that allows the derivation of fully functional services from existing forecasting or optimization code with ease. This work documents the strictly systematic development of the framework, beginning with requirement analysis, from which a sophisticated design concept is derived, followed by a description of the implementation of the framework. Furthermore, we present the concept of the Open Energy Services community, our effort to continuously maintain the service framework but also provide ready-to-use forecasting and optimization services. Finally, an evaluation of our framework and community concept, as well as a demarcation between our work and the current state of the art, is presented.

While the Sustainable Development Goals (SDGs) were being negotiated, global policymakers assumed that advances in data technology and statistical capabilities, what was dubbed the “data revolution”, would accelerate development outcomes by improving policy efficiency and accountability. The 2014 report to the United Nations Secretary General, “A World That Counts” framed the data-for-development agenda, and proposed four pathways to impact: measuring for accountability, generating disaggregated and real-time data supplies, improving policymaking, and implementing efficiency. The subsequent experience suggests that while many recommendations were implemented globally to advance the production of data and statistics, the impact on SDG outcomes has been inconsistent. Progress towards SDG targets has stalled despite advances in statistical systems capability, data production, and data analytics. The coherence of the SDG policy agenda has undoubtedly improved aspects of data collection and supply, with SDG frameworks standardizing greater indicator reporting. However, other events, including the response to COVID-19, have played catalytic roles in statistical system innovation. Overall, increased financing for statistical systems has not materialized, though planning and monitoring of these national systems may have longer-term impacts. This article reviews how assumptions about the data revolution have evolved and where new assumptions are necessary to advance the impact across the data value chain. These include focusing on measuring what matters most for decision-making needs across polycentric institutions, leveraging the SDGs for global data standardization and strategic financial mobilization, closing data gaps while enhancing policymaker analytic capabilities, and fostering collective intelligence to drive data innovation, credible information, and sustainable development outcomes.

This study analyzed standardized excess mortality due to specific causes during the Covid-19 pandemic across 33 European countries, using Eurostat data (2016–2021) and Our World in Data databases. Causes included circulatory and respiratory diseases, neoplasms, transport accidents, and “other” causes (e.g., diabetes, dementia, ill-defined conditions). Additional variables such as vaccination rates, economic and health indicators, demographics, and government stringency measures were also examined. Key findings include: (1) Most European countries (excluding Central and Eastern Europe), recorded lower than expected excess mortality from circulatory and respiratory diseases, neoplasms, and transport accidents. Ireland had the lowest excess respiratory mortality in both 2020 and 2021; (2) Croatia, Cyprus, Malta, and Turkey showed significant positive excess mortality from “other” causes, potentially linked to public health restrictions, with Turkey as an exception; (3) Regression analysis found that higher human development index and vaccination rates were associated with lower excess mortality. Policy Implications are: (1) Statistically significant positive or negative cause-specific excess mortality may indicate future health trends; (2) The pandemic and government stringency measures negatively affected mortality from “other” causes; (3) Strengthening health system resilience, investing in digital medicine, directing aid to countries with weaker systems, and supporting disadvantaged groups are key recommendations.

We propose a novel micro-level Cox model for incurred but not reported (IBNR) claims count based on hidden Markov models. Initially formulated as a continuous-time model, it addresses the complexity of incorporating temporal dependencies and policyholder risk attributes. However, the continuous-time model faces significant challenges in maximizing the likelihood and fitting right-truncated reporting delays. To overcome these issues, we introduce two discrete-time versions: one incorporating unsystematic randomness in reporting delays through a Dirichlet distribution and one without. We provide the EM algorithm for parameter estimation for all three models and apply them to an auto-insurance dataset to estimate IBNR claim counts. Our results show that while all models perform well, the discrete-time versions demonstrate superior performance by jointly modeling delay and frequency, with the Dirichlet-based model capturing additional variability in reporting delays. This approach enhances the accuracy and reliability of IBNR reserving, offering a flexible framework adaptable to different levels of granularity within an insurance portfolio.

We prove a Poisson process approximation result for stabilising functionals of a determinantal point process. Our results use concrete couplings of determinantal processes with different Palm measures and exploit their association properties. Second, we focus on the Ginibre process and show in the asymptotic scenario of an increasing observation window that the process of points with a large nearest neighbour distance converges after a suitable scaling to a Poisson point process. As a corollary, we obtain the scaling of the maximum nearest neighbour distance in the Ginibre process, which turns out to be different from its analogue for independent points.

Modeling detailed chemical kinetics is a primary challenge in combustion simulations. We present a novel framework to enforce physical constraints, specifically total mass and elemental conservation, during the reaction of ML models’ training for the reduced composition space chemical kinetics of large chemical mechanisms in combustion. In these models, the transport equations for a subset of representative species are solved with the ML approaches, while the remaining nonrepresentative species are “recovered” with a separate artificial neural network trained on data. Given the strong correlation between full and reduced solution vectors, our method utilizes a small neural network to establish an accurate and physically consistent mapping. By leveraging this mapping, we enforce physical constraints in the training process of the ML model for reduced composition space chemical kinetics. The framework is demonstrated here for methane, CH4, and oxidation. The resulting solution vectors from our deep operator networks (DeepONet)-based approach are accurate and align more consistently with physical laws.