Punching shear failure in slab-column connections is a brittle collapse mode that threatens the safety of flat reinforced concrete (RC) slabs. Conventional design provisions are generally conservative but exhibit inconsistencies across geometric and material variations. This study develops an eXtreme Gradient Boosting (XGBoost) model to predict the ultimate punching shear capacity of flat RC slabs, using a database of experimental results categorized by four different geometric domains, including square slab with square column, circular slab with circular column, square slab with circular column, and circular slab with square column, covering the geometric, materials strength, and reinforcement properties of input parameters. The model achieved high predictive accuracy across the domains with coefficient of determination (R2) values > 0.930 in unseen testing datasets with minimal bias (0.994–1.006) and reduced scatter. Model interpretability, addressed through the SHapley Additive exPlanations analysis, confirmed slab thickness and average effective depth as the most critical predictors of shear capacity, followed by concrete strength and reinforcement parameters, while boundary condition parameters showed negligible influence due to the predominance of interior column cases. These findings demonstrate that XGBoost provides accurate, reliable, and interpretable predictions of punching shear capacity, offering a data-driven alternative to code-based methods and supporting safer and more consistent design of flat RC slabs.

In this paper, we design a novel axiomatic approach to evaluating the joint risk of multiple insurance risks under dependence uncertainty. To be precise, we first establish a joint risk measure for non-negative multivariate risks, which we refer to as a (scalar) distortion joint risk measure. Then, we characterize it via a new set of axioms. Moreover, we introduce a new class of vector-valued distortion joint risk measures for non-negative multivariate risks and discuss their basic properties. Finally, comparisons with some existing vector-valued multivariate risk measures are made. It turns out that those vector-valued multivariate risk measures have forms of vector-valued distortion joint risk measures, respectively. This paper provides some relevant theoretical results about the evaluation of joint risk under dependence uncertainty.

This paper derives explicit expressions for drawdown-based two-sided exit identities involving the overshoots and undershoots at the exit times under Poisson observation times for spectrally negative Lévy risk processes by using fluctuation theory. All resulting Laplace transforms of the risk quantities of interest are expressed in terms of the scale functions of the spectrally negative Lévy processes.

Individual loss reserving methods have undergone substantial development in the past decade, driven by increased accessibility to granular-level insurance claims data. This paper presents a micro loss reserving model tailored for multi-coverage insurance policies, where a single insurance claim might trigger payments from multiple coverage types. We employ a copula-based multivariate regression approach to jointly model the settlement time and loss amount, effectively capturing the dependence among various types of loss amounts and their correlation with the settlement time. We stress the importance of considering both types of dependence for accurate reserving prediction and uncertainty quantification. Furthermore, we propose computationally efficient algorithms for parameter estimation and dynamic prediction. Through numerical experiments and real data analysis, we demonstrate the effectiveness of our proposed multivariate predictive model in loss reserving applications.

Sexually transmitted infections (STIs) are common among people living with human immunodeficiency virus (HIV) (PLWH). This nationwide register study linked HIV and STI registries to examine STI trends before and after HIV diagnosis in Finland 1995–2019 among all PLWH residing in the country. Analysed STIs were chlamydia, gonorrhoea, syphilis, and hepatitis B and C. An extended Cox model assessed factors associated with STI events. Among 3,775 PLWH (mean follow-up 17.9 person-years), 71% had no STIs, 17% had one, and 12% had two or more. Overall, 10.7% had an STI before HIV diagnosis and 18.1% after. STI incidence was 32 per 1,000 person-years and increased over time, although chlamydia and gonorrhoea declined. STI risk was highest among men who have sex with men (MSM) and lowest among people who inject drugs; it remained stable or declined after HIV diagnosis. STIs before HIV diagnosis offer opportunities for HIV testing and pre-exposure prophylaxis promotion. As most had no STIs other than HIV, HIV testing should not be limited to STI screening but also performed in other indicator conditions. After HIV diagnosis, accessible low-threshold STI testing, particularly for MSM, and consideration of doxycycline prophylaxis may benefit those at highest risk.

Distributed ledgers, including blockchain and other decentralized databases, are designed to store information online where all trusted network members can update the data with transparency. The dynamics of a ledger’s development can be mathematically represented by a directed acyclic graph (DAG). In this paper, we study a DAG model that considers batch arrivals and random delay of attachment. We analyze the asymptotic behavior of this model by letting the arrival rate go to infinity and the inter-arrival time go to zero. We establish that the number of leaves in the DAG, as well as various random variables characterizing the vertices in the DAG, can be approximated by its fluid limit, represented as the solution to a set of delayed partial differential equations. Furthermore, we establish the stable state of this fluid limit and validate our findings through simulations.

We consider shock models governed by the bivariate geometric counting process. By assuming the competing risks framework, failures are due to one of two mutually exclusive causes (shocks). We obtain and study some relevant functions, such as failure densities, survival functions, probability of the cause of failure, and moments of the failure time conditioned on a specific cause. Such functions are specified by assuming that systems or living organisms fail at the first instant in which a random threshold is reached by the sum of received shocks. Under this failure scheme, various cases arising for suitable choices of the random threshold are provided too.

The relevation model is a fundamental tool in reliability engineering for assessing the effectiveness of redundancy allocation in coherent systems. In this study, we address the problem of allocation of relevations for one or two nodes in a coherent system with independent components to enhance system reliability. We establish results concerning the usual stochastic and hazard rate orders for coherent systems. Moreover, we illustrate our findings with a range of examples and counterexamples. In addition, we conduct a simulation-based study and a real data analysis to further illustrate the application of our results. Lastly, we study the case of the minimal repair policy in detail.

This article proposes and studies two Huber-type estimation approaches, namely, the Huber instrumental variable (IV) estimation and the Huber generalized method of moments (GMM) estimation, for a spatial autoregressive model. We establish the consistency, asymptotic distributions, finite sample breakdown points, and influence functions of these estimators. Simulation studies show that compared to the corresponding traditional estimators (the two-stage least squares estimator, the best IV estimator, and the GMM estimator), our estimators are more robust when the unknown disturbances are long-tailed, and our estimators only lose a little efficiency when the disturbances are short-tailed. Moreover, the Huber GMM estimator also outperforms several robust estimators in the literature. Finally, we apply our estimation method to investigate the impact of the urban heat island effect on housing prices. A package is published on GitHub for practitioners to use in their empirical studies.

A general asymptotic theory is established for sample cross moments of nonstationary time series, allowing for long-range dependence and local unit roots. The theory provides a substantial extension of earlier results on nonparametric regression that include near-cointegrated nonparametric regression as well as spurious nonparametric regression. Many new models are covered by the limit theory, among which are functional coefficient regressions in which both regressors and the functional covariate are nonstationary. Simulations show finite sample performance matching well with the asymptotic theory and having broad relevance to applications, while revealing how dual nonstationarity in regressors and covariates raises sensitivity to bandwidth choice and the impact of dimensionality in nonparametric regression. An empirical example is provided involving climate data regression to assess Earth’s climate sensitivity to CO$_2$, where nonstationarity is a prominent feature of both the regressors and covariates in the model. To our knowledge, this application is the first nonparametric empirical analysis to assess potential nonlinear impacts of CO$_2$ on Earth’s climate while allowing for nonstationarity in both the regressors and covariates.