Deep geological repositories are critical for the long-term storage of hazardous materials, where understanding the mechanical behavior of emplacement drifts is essential for safety assurance. This study presents a surrogate modeling approach for the mechanical response of emplacement drifts in rock salt formations, utilizing Gaussian processes (GPs). The surrogate model serves as an efficient substitute for high-fidelity mechanical simulations in many-query scenarios, including time-dependent sensitivity analyses and calibration tasks. By significantly reducing computational demands, this approach facilitates faster design iterations and enhances the interpretation of monitoring data. The findings indicate that only a few key parameters are sufficient to accurately reflect in-situ conditions in complex rock salt models. Identifying these parameters is crucial for ensuring the reliability and safety of deep geological disposal systems.

Longevity risk is threatening the sustainability of traditional pension systems. To deal with this issue, decumulation strategies alternative to annuities have been proposed in the literature. However, heterogeneity in mortality experiences in the pool of policyholders due to socio-economic classes generates inequity, because of implicit wealth transfers from the more disadvantaged to the wealthier classes. We address this issue in a Group Self-Annuitization (GSA) scheme in the presence of stochastic mortality by proposing a redistributive GSA scheme where benefits are optimally shared across classes. The expected present values of the benefits in a standard GSA scheme show relevant gaps across socio-economic groups, which are reduced in the redistributive GSA scheme. We explore sensitivity to pool size, interest rates and mortality assumptions.

Vehicle telematics provides granular data for dynamic driving risk assessment, but current methods often rely on aggregated metrics (e.g., harsh braking counts) and do not fully exploit the rich time-series structure of telematics data. In this paper, we introduce a flexible framework using continuous-time hidden Markov model (CTHMM) to model and analyse trip-level telematics data. Unlike existing methods, the CTHMM models raw time-series data without predefined thresholds on harsh driving events or assumptions about accident probabilities. Moreover, our analysis is based solely on telematics data, requiring no traditional covariates such as driver or vehicle characteristics. Through unsupervised anomaly detection based on pseudo-residuals, we identify deviations from normal driving patterns—defined as the prevalent behaviour observed in a driver’s history or across the population—which are linked to accident risk. Validated on both controlled and real-world datasets, the CTHMM effectively detects abnormal driving behaviour and trips with increased accident likelihood. In real data analysis, higher anomaly levels in longitudinal and lateral accelerations consistently correlate with greater accident risk, with classification models using this information achieving ROC-AUC values as high as 0.86 for trip-level analysis and 0.78 for distinguishing drivers with claims. Furthermore, the methodology reveals significant behavioural differences between drivers with and without claims, offering valuable insights for insurance applications, accident analysis, and prevention.

We develop general conditions for weak convergence of adaptive Markov chain Monte Carlo processes and this is shown to imply a weak law of large numbers for bounded Lipschitz continuous functions. This allows an estimation theory for adaptive Markov chain Monte Carlo where previously developed theory in total variation may fail or be difficult to establish. Extensions of weak convergence to general Wasserstein distances are established, along with a weak law of large numbers for possibly unbounded Lipschitz functions. Applications are applied to autoregressive processes in various settings, unadjusted Langevin processes, and adaptive Metropolis–Hastings.

We study several basic problems about colouring the $p$-random subgraph $G_p$ of an arbitrary graph $G$, focusing primarily on the chromatic number and colouring number of $G_p$. In particular, we show that there exist infinitely many $k$-regular graphs $G$ for which the colouring number (i.e., degeneracy) of $G_{1/2}$ is at most $k/3 + o(k)$ with high probability, thus disproving the natural prediction that such random graphs must have colouring number at least $k/2 - o(k)$.

Consider nested subdivisions of a bounded real set into intervals defining the digits $X_1,X_2,\ldots$ of a random variable X with a probability density function f. If f is almost everywhere lower semi-continuous, there is a non-negative integer-valued random variable N such that the distribution of $R=(X_{N+1},X_{N+2},\ldots)$ conditioned on $S=(X_1,\ldots,X_N)$ does not depend on f. If also the lengths of the intervals exhibit a Markovian structure, $R\mid S$ becomes a Markov chain of a certain order $s\ge0$. If $s=0$ then $X_{N+1},X_{N+2},\ldots$ are independent and identically distributed with a known distribution. When $s>0$ and the Markov chain is uniformly geometric ergodic, there is a random time M such that the chain after time $\max\{N,s\}+M-s$ is stationary and M follows a simple known distribution.

We introduce a financial market model featuring a risky asset whose price follows a sticky geometric Brownian motion and a riskless asset that grows with a constant interest rate $r\in \mathbb R$. We prove that this model satisfies no arbitrage and no free lunch with vanishing risk only when $r=0$. Under this condition, we derive the corresponding arbitrage-free pricing equation, assess the replicability, and give a representation of the replication strategy. We then show that all locally bounded replicable payoffs for the standard Black–Scholes model are also replicable for the sticky model. Last, we evaluate via numerical experiments the impact of hedging in discrete time and of misrepresenting price stickiness.

Tunnel boring machines (TBMs) are essential equipment for tunnel excavation. The main component of TBMs for breaking rock is the disc cutter. The effectiveness and productivity of TBM operations are directly impacted by the disc cutter design and performance. This study investigates the effects of confining stress on the breaking force of disc cutters with various diameters. Both saturated and dry rock, such as low-strength concrete, medium-strength marble, and high-strength granite, are used in the tests. It is found that disc cutters with larger diameter can reduce the influence of the confining stress. Moreover, this research indicates that the influence of confining stress is more notable in rocks with higher strengths, especially in dry condition as opposed to saturated condition. The failure load is related to the confining stress, cutter diameter, and compressive strength of the rock in a multivariate linear regression model, suggesting that the confining stress is more significant than the other variables. These results highlight the importance of considering in-situ stress conditions when excavating tunnels by TBMs.

We focus on obtaining Block–Savits type characterizations for different ageing classes as well as some important renewal classes by using the Laplace transform. We also introduce a novel approach, based on the equilibrium distribution, to handle situations where the techniques of Block and Savits (1980) either fail or involve tedious calculations. Our approach in conjunction with the theory of total positivity yields Vinogradov’s (1973) result for the increasing failure rate class when the distribution function is continuous. We also present simple but elegant proofs for Block and Savits’ results for the decreasing mean residual life, new better than used in expectation, and harmonic new better than used in expectation classes as applications of our approach. We address several other related issues that are germane to our problem. Finally, we conclude with a short discussion on the issue of convolutions.

We all know the importance of taking advice from the right people. In the financial services arena, good quality financial advice helps millions of people manage their money, realise their goals and achieve long-term financial security. Given the low level of financial literacy in the general population, financial advice can be seen as a necessity for millions of people. However, our brief research to date indicates that a huge advice gap has emerged in the UK following the introduction of the Retail Distribution Review in 2012. And it doesn’t look like this will get better soon. Regarding this advice gap, the FCA stated in 2023 that “the provision of financial advice is often out of reach for all but the already wealthy.” Furthermore, efforts to mitigate the advice gap are either non-existent or piecemeal at best.

The consequences of the advice gap include poor investment decisions leading to sub-optimal savings: research shows that consumers in the UK who took professional financial advice between 2001 and 2006 enjoyed an average increase in their assets of nearly £48,000 (∼20%) after ten years, compared to those who took no advice. In this paper, we consider various mechanisms to improve access to financial advice and guidance and reduce the size of the advice gap while recognising that these should be implemented under the umbrella of Consumer Duty, which should ensure that customers receive good outcomes.

Finally, in terms of financial inclusion, there is a gender and social imbalance with women and those from lower socio-economic groups less likely to access formal advice or guidance. This is where the advice gap is biggest, and some of these groups of people could benefit most from access to financial advice or guidance, particularly those groups with lower levels of financial literacy who have reduced means to start off with. Implementing measures to reduce the advice gap under the umbrella of a strong consumer duty should provide customers with the means to pay for the level of advice that they need while being confident that they are receiving the right products for their needs at the right price leading to good customer outcomes.

On both global and local levels, one can observe a trend toward the adoption of algorithmic regulation in the public sector, with the Chinese social credit system (SCS) serving as a prominent and controversial example of this phenomenon. Within the SCS framework, cities play a pivotal role in its development and implementation, both as evaluators of individuals and enterprises and as subjects of evaluation themselves. This study engages in a comparative analysis of SCS scoring mechanisms for individuals and enterprises across diverse Chinese cities while also scrutinizing the scoring system applied to cities themselves. We investigate the extent of algorithmic regulation exercised through the SCS, elucidating its operational dynamics at the city level in China and assessing its interventionism, especially concerning the involvement of algorithms. Furthermore, we discuss ethical concerns surrounding the SCS’s implementation, particularly regarding transparency and fairness. By addressing these issues, this article contributes to two research domains: algorithmic regulation and discourse surrounding the SCS, offering valuable insights into the ongoing utilization of algorithmic regulation to tackle governance and societal challenges.

Africa had a busy election calendar in 2024, with at least 19 countries holding presidential or general elections. In a continent with a large youth population, a common theme across these countries is a desire for citizens to have their voices heard, and a busy election year offers an opportunity for the continent to redeem its democratic credentials and demonstrate its leaning towards strengthening free and fair elections and a more responsive and democratic governance. Given the central role that governance plays in security in Africa, the stakes from many of these elections are high, not only to achieve a democratically elected government but also to achieve stability and development. Since governance norms, insecurity, and economic buoyancy are rarely contained by borders, the conduct and outcomes from each of these elections will also have implications for neighbouring countries and the continent overall. This article considers how the results of recent elections across Africa have been challenged in courts based on mistrust in the use of technology platforms, how the deployment of emerging technology, including AI, is casting a shadow on the integrity of elections in Africa, and the policy options to address these emerging trends with a particular focus on governance of AI technologies through a human rights-based approach and equitable public procurement practices.