We consider a nonnegative random variable T representing the lifetime of a system. We discuss the residual lifetime $T_X=(T-X|T \gt X)$, where X denotes the random age of the system. We also discuss the mean residual life (MRL) of T at the random time X. It is shown that the MRL at random age (MRLR) is closely related to some well-known variability measures. In particular, we show that the MRLR can be considered a generalization of Gini’s mean difference (GMD). Under the proportional hazards model, we show that the MRLR gives the extended GMD and the extended cumulative residual entropy as special cases. Then, we provide a decomposition result indicating that the MRLR has a covariance representation. Some comparison results are also established for the MRLs of two systems at random ages.

Due to the risk of Shiga-toxin producing Escherichia coli (STEC) transmission, current guidance advises excluding young children from childcare settings until microbiologically clear. Children can shed STEC for a prolonged period, and the cost-effectiveness of exclusion has not been evaluated. Our decision tree analysis, including probabilistic sensitivity analysis, estimated comparative health system costs and effects of exclusion until microbiological clearance versus return to childcare setting before this. Due to the risk of secondary cases, return before microbiological clearance resulted in the incremental loss of 0.019 QALYs, but savings of £156. Using the willingness-to-pay threshold of £20000 per QALY, the incremental net monetary benefit of exclusion until microbiological clearance was £215. Exclusion until microbiological clearance remained cost-effective if the total costs for managing the exclusion were below £576. Return before microbiological clearance may, therefore, become cost-effective in cases where the costs of managing exclusion until microbiological clearance are high and/or the risk of secondary cases is very low. Broadening the decision perspective, including the costs of exclusion to the families, may also impact the recommendation. Further research is needed to assess the risk of STEC transmission from children who have clinically recovered and the impact of STEC and exclusion on families of the affected children.

We consider pricing of a specialised critical illness and life insurance contract for breast cancer (BC) risk. We compare (a) an industry-based Markov model with (b) a recently developed semi-Markov model, which accounts for unobserved BC cases and progression through clinical stages of BC, and (c) an alternative Markov model derived from (b). All models are calibrated using population data in England and data from the medical literature. We show that the semi-Markov model aligns best with empirical evidence. We then consider net premiums of specialized life insurance products under various scenarios of cancer diagnosis and treatment. The results show strong dependence on the time spent with diagnosed or undiagnosed pre-metastatic BC. This proves to be significant for refining cancer survival estimates and accurately estimating related age dependence by cancer stage. In contrast, the industry-based model, by overlooking this critical factor, is more sensitive to the model assumptions, underscoring its limitations in cancer estimates.

We establish a number of results concerning the limiting behaviour of the longest edges in the genealogical tree generated by a continuous-time Galton–Watson process. Separately, we consider the large-time behaviour of the longest pendant edges, the longest (strictly) interior edges, and the longest of all the edges. These results extend the special case of long pendant edges of birth–death processes established in Bocharov et al. (2023).

This study proposes a test for coefficient randomness in autoregressive models where the autoregressive coefficient is local to unity, which is empirically relevant given earlier work. Under this specification, we analyze the effect of the correlation between the random coefficient and disturbance on the properties of tests, a matter that remains largely unexplored in the literature. Our analysis reveals that tests proposed in earlier studies can have poor power when the correlation is moderate to large. The test proposed here is designed to have power functions robust to the correlation. A modified version of the test is suggested that can be applied when the disturbance is serially correlated and conditionally heteroskedastic. The test is shown to have better power properties than existing ones in large and finite samples.

This study analyzes National Cyber Security Strategies (NCSSs) of G20 countries through a novel combination of qualitative and quantitative methodologies. It focuses on delineating the shared objectives, distinct priorities, latent themes, and key priorities within the NCSSs. Latent dirichlet allocation topic modeling technique was used to identify implicit themes in the NCSSs to augment the explicitly articulated strategies. By exploring the latest versions of NCSS documents, the research uncovers a detailed panorama of multinational cybersecurity dynamics, offering insights into the complexities of shared and unique national cybersecurity challenges. Although challenged by the translation of non-English documents and the intrinsic limitations of topic modeling, the study significantly contributes to the cybersecurity policy domain, suggesting directions for future research to broaden the analytical scope and incorporate more diverse national contexts. In essence, this research underscores the indispensability of a multifaceted, analytical approach in understanding and devising NCSSs, vital for navigating the complex, and ever-changing digital threat environment.

Recent developments in national health data platforms have the potential to significantly advance medical research, improve public health outcomes, and foster public trust in data governance. Across Europe, initiatives such as the NHS Research Secure Data Environment in England and the Data Room for Health-Related Research in Switzerland are underway, reflecting examples analogous to the European Health Data Space in two non-EU nations. Policy discussions in England and Switzerland emphasize building public trust to foster participation and ensure the success of these platforms. Central to building public trust is investing efforts into developing and implementing public involvement activities. In this commentary, we refer to three national research programs, namely the UK Biobank, Genomics England, and the Swiss Health Study, which implemented effective public involvement activities and achieved high participation rates. The public involvement activities used within these programs are presented following on established guiding principles for fostering public trust in health data research. Under this lens, we provide actionable policy recommendations to inform the development of trust-building public involvement activities for national health data platforms.

The REDATAM (retrieval of data for small areas by microcomputer) statistical package and format, developed by ECLAC, has been a critical tool for disseminating census data across Latin America since the 1990s. However, significant limitations persist, including its proprietary nature, lack of documentation, and restricted flexibility for advanced data analysis. These challenges hinder the transformation of raw census data into actionable information for policymakers, researchers, and advocacy groups. To address these issues, we developed Open REDATAM, an open-source and multiplatform tool that converts REDATAM data into widely supported CSV files and native R and Python data structures. By providing integration with R and Python, Open REDATAM empowers users to work with the tools they already know and perform data analyses without leaving their R or Python window. Our work emphasizes the need for a REDATAM official format specification to further enable informed policy debates that can improve policy processes’ implementation and feedback.

As data becomes a key component of urban governance, the night-time economy is still barely visible in datasets or in policies to improve urban life. In the last 20 years, over 50 cities worldwide appointed night mayors and governance mechanisms to tackle conflicts, foster innovation, and help the night-time economy sector grow. However, the intersection of data, digital rights, and 24-hour cities still needs more studies, examples, and policies. Here, the key argument is that the increasing importance of the urban night in academia and local governments claims for much-needed responsible data practices to support and protect nightlife ecosystems. By understanding these ecosystems and addressing data invisibilities, it is possible to develop a robust framework anchored in safeguarding human rights in the digital space and create comprehensive policies to help such ecosystems thrive. Night-time governance matters for the data policy community for three reasons. First, it brings together issues covered in different disciplines by various stakeholders. We need to build bridges between sectors to avoid siloed views of urban data governance. Second, thinking about data in cities also means considering the social, economic, and cultural impact of datafication and artificial intelligence on a 24-hour cycle. Creating a digital rights framework for the night means putting into practice principles of justice, ethics, and responsibility. Third, as Night Studies is an emerging field of research, policy and advocacy, there is an opportunity to help shape how, why, and when data about the night is collected and made available to society.

In the reliability analysis of multicomponent stress-strength models, it is typically assumed that strengths are either independent or dependent on a common stress factor. However, this assumption may not hold true in certain scenarios. Therefore, accurately estimating the reliability of the stress-strength model becomes a significant concern when strengths exhibit interdependence with both each other and the common stress factor. To address this issue, we propose an Archimedean copula (AC)-based hierarchical dependence approach to effectively model these interdependencies. We employ four distinct semi-parametric methods to comprehensively estimate the reliability of the multicomponent stress-strength model and determine associated dependence parameters. Furthermore, we derive asymptotic properties of our estimator and demonstrate its effectiveness through both Monte Carlo simulations and real-life datasets. The main original contribution of this study is the first attempt to evaluate the reliability problem under dependent strengths and stress using a hierarchical AC approach.

What drives changes in the thematic focus of state-linked manipulated media? We study this question in relation to a long-running Iranian state-linked manipulated media campaign that was uncovered by Twitter in 2021. Using a variety of machine learning methods, we uncover and analyze how this manipulation campaign’s topical themes changed in relation to rising Covid-19 cases in Iran. By using the topics of the tweets in a novel way, we find that increases in domestic Covid-19 cases engendered a shift in Iran’s manipulated media focus away from Covid-19 themes and toward international finance- and investment-focused themes. These findings underscore (i) the potential for state-linked manipulated media campaigns to be used for diversionary purposes and (ii) the promise of machine learning methods for detecting such behaviors.

The Least Trimmed Squares (LTS) regression estimator is known to be very robust to the presence of “outliers”. It is based on a clear and intuitive idea: in a sample of size n, it searches for the h-subsample of observations with the smallest sum of squared residuals. The remaining $n-h$ observations are declared “outliers”. Fast algorithms for its computation exist. Nevertheless, the existing asymptotic theory for LTS, based on the traditional $\epsilon $-contamination model, shows that the asymptotic behavior of both regression and scale estimators depend on nuisance parameters. Using a recently proposed new model, in which the LTS estimator is maximum likelihood, we show that the asymptotic behavior of both the LTS regression and scale estimators are free of nuisance parameters. Thus, with the new model as a benchmark, standard inference procedures apply while allowing a broad range of contamination.

We model voting behaviour in the multi-group setting of a two-tier voting system using sequences of de Finetti measures. Our model is defined by using the de Finetti representation of a probability measure (i.e. as a mixture of conditionally independent probability measures) describing voting behaviour. The de Finetti measure describes the interaction between voters and possible outside influences on them. We assume that for each population size there is a (potentially) different de Finetti measure, and as the population grows, the sequence of de Finetti measures converges weakly to the Dirac measure at the origin, representing a tendency toward weakening social cohesion as the population grows large. The resulting model covers a wide variety of behaviours, ranging from independent voting in the limit under fast convergence, a critical convergence speed with its own pattern of behaviour, to a subcritical convergence speed which yields a model in line with empirical evidence of real-world voting data, contrary to previous probabilistic models used in the study of voting. These models can be used, e.g., to study the problem of optimal voting weights in two-tier voting systems.

The principal function of an open recirculating system (ORS) is to remove heat from power plant equipment. In particular, the presence of scale on the internal surfaces of ORS heat exchange equipment can reduce heat transfer efficiency, which leads to increased energy consumption and operating costs. The purpose of this article is to investigate the process of calcium carbonate (CaCO3) precipitation formation in terms of the components of the carbonate system and parameters affecting the shift of carbonate equilibrium in an ORS. An appraisal model was used to represent the processes occurring during the operation of an ORS. In this study, it is demonstrated that water heating in ORS condensers leads to the excretion of carbon dioxide (CO2) from the water, while cooling in the cooling towers results in CO2 uptake by the water. These processes significantly influence the state of carbonate equilibrium within the ORS. The study used the results of chemical control of the make-up and cooling water at the ORS Rivne Nuclear Power Plant (RNPP) for 2022. Furthermore, the dependencies of changes in the components of the carbonate system on the pH levels of the make-up (pH 7.51–9.52) and cooling (pH 8.21–9.53) water were revealed, and changes in the cycles of concentration (CоC), total hardness (TH), total dissolved solids (TSD), and total alkalinity (TA) were estimated. Taking into account the obtained correlation dependencies, in general, it was found that the lower the CoC levels, the lower the TA reduction value, and it is possible to increase or decrease the cooling water pH levels, which is determined by the initial state of carbonate equilibrium of make-up water. These findings enable the prediction and control of CaCO3 scale formation through continuous monitoring of water chemistry, making the process more efficient, reliable, and sustainable. The results emphasize the importance of data-driven modeling for optimizing water treatment and reducing operational costs in power plants by reducing CaCO3 scale formation.

This article examines high-dimensional covariates in regression discontinuity design (RDD) analysis. We introduce estimation and inference methods for the RDD models that incorporate covariate selection while maintaining stability across various numbers of covariates. The proposed methods combine a localization approach using kernel weights with $\ell _{1}$-penalization to handle high-dimensional covariates. We provide both theoretical and numerical evidence demonstrating the efficacy of our methods. Theoretically, we present risk and coverage properties for our point estimation and inference methods. Conditions are given under which the proposed estimator becomes more efficient than the conventional covariate adjusted estimator at the cost of an additional sparsity condition. Numerically, our simulation experiments and empirical examples show the robust behaviors of the proposed methods to the number of covariates in terms of bias and variance for point estimation and coverage probability and interval length for inference.

The complex socioeconomic landscape of conflict zones demands innovative approaches to assess and predict vulnerabilities for crafting and implementing effective policies by the United Nations (UN) institutions. This article presents a groundbreaking Augmented Intelligence-driven Prediction Model developed to forecast multidimensional vulnerability levels (MVLs) across Afghanistan. Leveraging a symbiotic fusion of human expertise and machine capabilities (e.g., artificial intelligence), the model demonstrates a predictive accuracy ranging between 70% and 80%. This research not only contributes to enhancing the UN Early Warning (EW) Mechanisms but also underscores the potential of augmented intelligence in addressing intricate challenges in conflict-ridden regions. This article outlines the use of augmented intelligence methodology applied to a use case to predict MVLs in Afghanistan. It discusses the key findings of the pilot project, and further proposes a holistic platform to enhance policy decisions through augmented intelligence, including an EW mechanism to significantly improve EW processes, thereby supporting decision-makers in formulating effective policies and fostering sustainable development within the UN.

This paper defines and studies a broad class of shock models by assuming that a Markovian arrival process models the arrival pattern of shocks. Under the defined class, we show that the system’s lifetime follows the well-known phase-type distribution. Further, we examine the age replacement policy for systems with a continuous phase-type distribution, identifying sufficient conditions for determining the optimal replacement time. Since phase-type distributions are dense in the class of lifetime distributions, our findings for the age replacement policy are widely applicable. We include numerical examples and graphical illustrations to support our results.

We study the Markov chain Monte Carlo estimator for numerical integration for functions that do not need to be square integrable with respect to the invariant distribution. For chains with a spectral gap we show that the absolute mean error for $L^p$ functions, with $p \in (1,2)$, decreases like $n^{({1}/{p}) -1}$, which is known to be the optimal rate. This improves currently known results where an additional parameter $\delta \gt 0$ appears and the convergence is of order $n^{(({1+\delta})/{p})-1}$.

In February 2023, 52 cases of gastrointestinal illness were reported in customers of Takeaway A, South Wales. Shigella flexneri serotype 2a was the causative organism. An outbreak investigation was conducted to determine the extent and vehicle of the outbreak.

Following descriptive summary and environmental investigations, a case–control study was completed. Participants completed a telephone questionnaire on food, travel, and environmental exposures. A multivariable logistic regression model was built, including exposures with p-values < 0.2 and interactions identified on stratified analysis. Staff faecal samples were screened for Shigella sp.

Thirty-one cases and 29 controls were included in the study. Eighty-seven per cent of cases and 76% of controls ate from Takeaway A on 10 February 2023. Coleslaw was the main factor associated with illness (aOR: 200, 95% CI: 12–3220) and an interaction with cabbage was identified (aOR: 886, 95% CI: 26–30034). Shigella sp. were not detected in any staff samples.

Coleslaw was the most likely vehicle. Though the contamination route is unknown, a food handler is the most likely source. This large outbreak differs from recent European outbreaks, which primarily have been associated with sexual transmission. Although uncommon in the UK, S. flexneri should be considered as a cause of foodborne outbreaks.