We study a queueing system with a fixed number of parallel service stations of infinite servers, each having a dedicated arrival process, and one flexible arrival stream that is routed to one of the service stations according to a ‘weighted’ shortest queue policy. We consider the model with general arrival processes and general service time distributions. Assuming that the dedicated arrival rates are of order n and the flexible arrival rate is of order $\sqrt{n}$, we show that the diffusion-scaled queueing processes converge to a stochastic Volterra integral equation with ‘ranks’ driven by a continuous Gaussian process. It reduces to the limiting diffusion with a discontinuous drift in the Markovian setting.

Belief network analysis (BNA) has enabled major advances in the study of belief systems, capturing Converse’s understanding of the interdependence among multiple beliefs (i.e., constraint) more intuitively than many conventional statistics. However, BNA struggles with representing political divisions that follow a spatial logic, such as the “left–right” or “liberal-conservative” ideological divide. We argue that Response Item Networks (ResINs) have important advantages for modeling political cleavage lines as they organically capture belief systems in a latent ideological space. In addition to retaining many desirable properties inherent to BNA, ResIN can uncover ideological polarization in a visually intuitive, theoretically grounded, and statistically robust fashion. We demonstrate the advantages of ResIN by analyzing ideological polarization with regard to five hot-button issues from 2000 to 2020 using the American National Election Studies (ANES), and by comparing it against an equivalent procedure using BNA. We further introduce system-level and attitude-level polarization measures afforded by ResIN and discuss their potential to enrich the analysis of ideological polarization. Our analysis shows that ResIN allows us to observe much more detailed dynamics of polarization than classic BNA approaches.

This article examines the implications of adopting a socio-technical perspective on the design and implementation of GovTech solutions. To observe the phenomenon, it adopts a case study approach focusing on the WiseTown solution and its City Digital Twin (CDT), developed by the Italian company TeamDev. The article investigates how integrating social factors, such as urban governance, with technical elements, like data analysis and modeling, can enhance the conceptualization, design, and implementation of user-centric, data-driven digital solutions as part of a broader digital transformation strategy. The article explores an Italian best practice that is developing four dimensions of the GovTech socio-technical framework: Governance Structures, Institutional Arrangements, User and Context Understanding, and Technological Development. It critically examines and discusses the challenges and opportunities associated with the adoption of CDTs and their impact on public policy implementation. The analysis is centered on two main aspects that emerged from the case study: data integration and sharing within CDTs, and the social implications associated with data usage for decision-making. Ultimately, the article explores the role of stakeholder collaboration (public-private partnerships) and the creation of innovation ecosystems—GovTech ecosystem in this specific case—to inform and steer policymaking through and beyond the adoption of CDTs.

In many African countries, limited population data pose a challenge for tax administrations struggling with informal economies. This study examines Uganda’s integration of national ID data into tax registration through “Instant TIN,” an interface linking the Uganda Revenue Authority (URA) with the National Identification and Registration Agency (NIRA) and the Uganda Registration Service Bureau (URSB). This reform aims to streamline taxpayer registration and improve data quality. Using a mixed-methods approach—combining interviews with government officials and administrative data analysis—we identify three key findings. First, Instant TIN registrants differ significantly from those using the conventional process. They are more likely to be individuals, female, younger, and previously informal, highlighting the reform’s role in bringing in marginalised taxpayers. Second, Instant TIN improves data quality. It reduces TIN duplications for individuals and enhances contact accuracy, decreasing invalid or missing email addresses by eight percentage points and invalid phone numbers by six. However, it worsens sector data quality, increasing missing or incorrect sector information by 12 percentage points. Third, while Instant TIN reduces duplication, manual data entry, and administrative burdens, challenges remain. Infrequent updates in external datasets and a lack of validation within the interface increase administrative costs and complicate taxpayer engagement. Additionally, mandatory in-person updates and invalid contact details add to compliance burdens. Overall, Uganda’s experience highlights both the potential and limitations of integrating national ID data for tax administration, offering insights for other countries considering similar reforms.

How can we make global sensitivity analysis accessible and viable for engineering practice? In this translation article, we present a methodology to enable sensitivity analysis for structural and geotechnical engineering for built environment design and assessment workflows. Our technique wraps computational mechanics and geomechanics finite element (FE) simulations and combines high-performance computing on public cloud with surrogate modeling using machine learning. A key question we address is: “Is there a noticeable loss in fidelity of results from the sensitivity analysis when substituting a simulation model with a surrogate model?” We answer this question for both linear and nonlinear FE simulations.

In 1976, Cameron, Goethals, Seidel, and Shult classified all the graphs whose smallest eigenvalue is at least $-2$ by relating such graphs to root systems that appear in the classification of semisimple Lie algebras. In this paper, extending their beautiful theorem, we give a complete classification of all connected graphs whose smallest eigenvalue lies in $(\! -\lambda ^*, -2)$, where $\lambda ^* = ho ^{1/2} + ho ^{-1/2} \approx 2.01980$, and $ho$ is the unique real root of $x^3 = x + 1$. Our result is the first classification of infinitely many connected graphs with their smallest eigenvalue in $(\! -\lambda , -2)$ for any constant $\lambda \gt 2$.

In machine learning-based mortality models, interpretation methods are well established, and they can reveal structures resembling the age or time effects in traditional mortality models. However, in the reverse direction, using such traditional components to guide the initialization of a neural network remains highly challenging due to information loss during model interpretation. This study addresses this gap by exploring how components from pre-fitted traditional mortality models can be used to initialize neural networks, enabling structural information to be incorporated into a deep learning framework. We introduce Kolmogorov–Arnold Networks (KAN) and first construct two shallow models, KAN[2,1] and ARIMAKAN, to examine their applicability to mortality modeling. We then extend the Combined Actuarial Neural Network (CANN) into a KAN-based Actuarial Neural Network (KANN), in which classical model components calibrated via generalized nonlinear models or generalized additive models are naturally used for initialization. Three KANN variants, namely KANN[2,1], KANNLC, and KANNAPC, are proposed. In these models, neural networks assist in improving the accuracy of traditional models and help refine the original parameter estimates. All KANN-based models can also produce smooth mortality curves as well as smooth age, period, and cohort effects through simple regularization. Experiments on 34 populations demonstrate that KAN-based approaches achieve stable performance while balancing interpretability, smoothness, and predictive accuracy.

Google Trends is used in research and surveillance as a proxy for community infection incidence. Signals are difficult to validate, as most surveillance biases towards severe outcomes and certain demographics.

Using Winter COVID-19 Infection Study (WCIS) data in England, symptom prevalence is estimated via generalized additive model with multilevel-regression and poststratification. Symptom duration was estimated using interval censored time delay modelling, converting prevalence to incidence. Google Trends and WCIS incidence and growth rates were compared using cross-correlation.

Google Trends and WCIS agreement varied by symptom and age group. The national maximum growth rate cross-correlation for sore throat was 0.81, with 90% prediction intervals of [0.69, 0.90]. Google Trends growth rates generally lagged the WCIS growth rates across symptoms (cough: −5.0 days [−8.0, 0.0], fever: −3.0 days [−6.0, 1.0], loss of smell: −9.0 days [−13, −3.0], shortness of breath: −12 days [−16, −5.0], and sore throat: −4.0 days [−5.0, −2.0]).

This work shows Google Trends and community symptom incidence can align, although substantial variation between symptoms and age groups exists, underscoring utility in predicting other surveillance indicators.

It is well known that almost all graphs are canonizable by a simple combinatorial routine known as colour refinement, also referred to as the 1-dimensional Weisfeiler–Leman algorithm. With high probability, this method assigns a unique label to each vertex of a random input graph and, hence, it is applicable only to asymmetric graphs. The strength of combinatorial refinement techniques becomes a subtle issue if the input graphs are highly symmetric. We prove that the combination of colour refinement and vertex individualization yields a canonical labelling for almost all circulant digraphs (i.e., Cayley digraphs of a cyclic group). This result provides first evidence of good average-case performance of combinatorial refinement within the class of vertex-transitive graphs. Remarkably, we do not even need the full power of the colour refinement algorithm. We show that the canonical label of a vertex $v$ can be obtained just by counting walks of each length from $v$ to an individualized vertex. Our analysis also implies that almost all circulant graphs are compact in the sense of Tinhofer, that is, their polytops of fractional automorphisms are integral. Finally, we show that a canonical Cayley representation can be constructed for almost all circulant graphs by the more powerful 2-dimensional Weisfeiler–Leman algorithm.

Cyber risk is an important consideration in today’s risk management and insurance industries. However, the statistical features of cyber risk, including concerns of solvency for cyber insurance providers, are still emerging. This study investigates the dynamics of ransomware severity, specifically focusing on different statistical dimensions of extortion payments from ransomware attacks across various ransomware strains and/or variants. Our results indicate that extortion payments are not identically distributed across ransomware strains/variants, and thus violate necessary assumptions for solvency determinations using classical ruin theory. These findings emphasize the importance of re-examining these assumptions under empirical data and implementing dynamic cyber risk modelling for portfolio losses from extortion payments from ransomware attacks. Additionally, such findings suggest that removing coverage for extortion payments from insurance policies may protect cyber insurance firms from insolvency, as well as create a potential deterrence effect against ransomware threat actors due to lack of extortion payment from victims. Our work has implications for insurance regulators, policymakers, and national security advisors focused on the financial impact of extortion payments from ransomware attacks.

Any margin of the multinomial distribution is multinomially distributed. Retaining this closure property, a family of generalized multinomial distributions is proposed. This family is characterized within multiplicative probability measures, using the Bell polynomial. The retained closure property simplifies marginal properties such as moments. The family can be obtained by conditioning independent infinitely divisible distributions on the total and also by mixing the multinomial distribution with the normalized infinitely divisible distribution. The closure property justifies a stochastic process of the family by Kolmogorov’s extension theorem. Over time, Gibbs partitions of a positive integer appear as the limiting distributions of the family.

We sought to identify risk factors for coagulase-negative staphylococcal (CoNS) surgical site infection (SSI). Risk factors associated with an increased risk of CoNS SSI include male sex and asthma or COPD. Colon surgery was associated with a reduced risk of CoNS SSI.