Distribution channels such as bancassurance, brokers, agents, direct online sales, and insurance aggregators have been key to ensuring premium growth for both life and non-life insurers. However in recent years, an emerging channel known as embedded insurance has started to provide insurers with a brand-new growth driver. In this paper, we first present an introduction to embedded insurance – what it is and how it will shape insurance distribution in the industry. We then introduce a framework to classify embedded insurance recommendation system. Finally, we propose a novel insurance recommendation system using supervised learning algorithms that can be applied to e-commerce platforms. This needs-based collaborative filtering technique recommends one of three insurance products that would be most appropriate for each buyer on the Olist e-commerce platform based on order-level data. Our work is relevant for actuaries in this field interested in the pricing of embedded insurance risk as well as insurers seeking to improve insurance penetration on such platforms.

Monkeypox (mpox) has re-emerged as global public health concern including in several non-endemic countries. This study aims to characterize monkeypox virus (MPXV) genomes in Indonesia, to explore viral evolution and transmission. Genomic analysis was conducted on 53 isolates from Indonesian mpox patients between 2023 and 2024. All sequences belonged to Clade IIb, with identified sub-clades including A.1.1, B.1, B.1.3, and C.1 – of which C.1 became dominant during this period. Out of 87 mpox-confirmed cases, 60.9% (53/87) were successfully sequenced and submitted to the Global Initiative on Sharing All Influenza Data (GISAID). The majority of cases in Indonesia occurred among males (95.4%), men who have sex with men (59.8%), and people living with HIV/AIDS (71.3%). Notably, a large portion of cases had no travel history, suggesting local transmission. Initially, only clade IIb (B.1) was detected in October 2022. By August 2023, lineage diversity had increased, with B.1.3 and C.1 emerging as the predominant sub-clades. A time–calibrated phylogenetic tree revealed genetic relatedness and shared ancestry within clade IIb. Integrating genomic and epidemiological data offers valuable insights to improve mpox surveillance and public health response in Indonesia and the broader region

The coupling of the disruptive processes of digitalization and the green transformation in a so-called “Twin Transformation” is already being considered a strategic step within the European Union and is discussed in the academic sphere. Strategically, this coupling is necessary and meaningful to realize synergies and to avoid counterproductive effects, such as rebound effects or lock-in effects, particularly given the time constraints imposed by climate change. The European data strategy not only calls for the establishment of various data spaces, such as the data space for the European Green New Deal, but also calls for the opening, integration, and utilization of European data for stakeholders from administration, business, and civil society. Considering this, it is argued that administrative informatics as a discipline could be integrated as an additional analytical perspective into the political science heuristic of the policy cycle. This integration offers substantial added value for analyzing and shaping the policy processes of the European Green transformation. Moreover, this heuristic approach enables the ex-ante prediction of changes in policymaking based on the theories, models, methods, and application areas of administrative informatics. Building on this premise, this article provides insights into the application of the proposed heuristic using the example of the European Green transformation. It analyzes the resulting implications for the analysis of policymaking considering an increasingly digitalized public administration.

We initiate a study of large deviations for block model random graphs in the dense regime. Following [14], we establish an LDP for dense block models, viewed as random graphons. As an application of our result, we study upper tail large deviations for homomorphism densities of regular graphs. We identify the existence of a ‘symmetric’ phase, where the graph, conditioned on the rare event, looks like a block model with the same block sizes as the generating graphon. In specific examples, we also identify the existence of a ‘symmetry breaking’ regime, where the conditional structure is not a block model with compatible dimensions. This identifies a ‘reentrant phase transition’ phenomenon for this problem – analogous to one established for Erdős–Rényi random graphs [13, 14]. Finally, extending the analysis of [34], we identify the precise boundary between the symmetry and symmetry breaking regimes for homomorphism densities of regular graphs and the operator norm on Erdős–Rényi bipartite graphs.

Dengue, the most prevalent urban arbovirus in the world, has triggered recurrent epidemics in Rio de Janeiro, Brazil, since the 1980s. This study aimed to describe the spatial–temporal patterns of dengue spread during the epidemic years of 2002, 2008, 2011, 2012, 2013, and 2024 in Rio de Janeiro. This is an ecological study using secondary data on notified confirmed dengue cases aggregated by neighbourhood. The incidence rates were estimated via the local empirical Bayes method. The local spatial autocorrelation indicators assessed incidence clusters, and the monthly geographic trajectory was outlined for each year. The results revealed changes in the spatial distribution of dengue over time, with clusters of high incidences predominating in the northern and central neighbourhoods in 2002 and 2008, and in the western zone in 2011, 2012, and 2013. In 2024, the distribution was predominant throughout the city, with emphasis in the central and western zones. The monthly geographic centre of dengue cases shifted from the west to the north during the peak of the epidemic. These results highlight the heterogeneous nature of dengue transmission in Rio de Janeiro. The incorporation of spatial and temporal analyses in epidemiological studies can enhance targeted and localized dengue control strategies.

A graph $H$ is said to be common if the number of monochromatic labelled copies of $H$ in a red/blue edge colouring of a large complete graph is asymptotically minimised by a random colouring in which each edge is equally likely to be red or blue. We extend this notion to an off-diagonal setting. That is, we define a pair $(H_1,H_2)$ of graphs to be $(p,1-p)$-common if a particular linear combination of the density of $H_1$ in red and $H_2$ in blue is asymptotically minimised by a random colouring in which each edge is coloured red with probability $p$ and blue with probability $1-p$. Our results include off-diagonal extensions of several standard theorems on common graphs and novel results for common pairs of graphs with no natural analogue in the classical setting.

Global food security worsened during the COVID-19 pandemic. In Nigeria, food security indicators increased in the first months of the pandemic and then decreased slightly but never returned to their pre-pandemic levels. We assess if savings groups provided household coping mechanisms during COVID-19 in Nigeria by combining the in-person LSMS-ISA/GHS-2018/19 with four rounds of the Nigerian Longitudinal Phone Survey collected during the first year of the pandemic. A quasi-difference-in-differences analysis setup leveraging the panel nature of the data indicates that savings group membership reduces the likelihood of skipping a meal but finds no statistically significant effect on the likelihood of running out of food or eating fewer kinds of food. Given theoretical priors and other literature positing a relationship, we also implement an OLS regression analysis controlling for baseline values finding that having at least one female household member in a savings group is associated with a 5–15% reduction in the likelihood of reporting skipping meals, running out of food, and eating fewer kinds of food. This analysis is not able to establish causality, however, and may in fact overestimate the effects. Together, the results indicate that savings group membership is positively associated with food security after COVID-19, but the causal effect is statistically significant for only one of the three food security indicators. To conclude, considering the interest in savings groups and expectations of continued food security shocks, the importance of collecting better gender-disaggregated longitudinal household data combined with experimental designs and institutional data on savings groups.

Heating, Ventilation, and Air Conditioning (HVAC) systems are major energy consumers in buildings, challenging the balance between efficiency and occupant comfort. While prior research explored generative AI for HVAC control in simulations, real-world validation remained scarce. This study addresses this gap by designing, deploying, and evaluating “Office-in-the-Loop,” a novel cyber-physical system leveraging generative AI within an operational office setting. Capitalizing on multimodal foundation models and Agentic AI, our system integrates real-time environmental sensor data (temperature, occupancy, etc.), occupants’ subjective thermal comfort feedback, and historical context as input prompts for the generative AI to dynamically predict optimal HVAC temperature setpoints. Extensive real-world experiments demonstrate significant energy savings (up to 47.92%) while simultaneously improving comfort (up to 26.36%) compared to baseline operation. Regression analysis confirmed the robustness of our approach against confounding variables like outdoor conditions and occupancy levels. Furthermore, we introduce Data-Driven Reasoning using Agentic AI, finding that prompting the AI for data-grounded rationales significantly enhances prediction stability and enables the inference of system dynamics and cost functions, bypassing the need for traditional reinforcement learning paradigms. This work bridges simulation and reality, showcasing generative AI’s potential for efficient, comfortable building environments and indicating future scalability to large systems like data centers.

In this editorial, we draw insights from a special collection of peer-reviewed papers investigating how new data sources and technology can enhance peace. The collection examines local and global practices that strive towards positive peace through the responsible use of frontier technologies. In particular, the articles of the collection illustrate how advanced techniques—including machine learning, network analysis, specialised text classifiers, and large-scale predictive analytics—can deepen our understanding of conflict dynamics by revealing subtle interdependencies and patterns. Others assess innovative approaches reinterpreting peace as a relational phenomenon. Collectively, they assess ethical, technical, and governance challenges while advocating balanced frameworks that ensure accountability alongside innovation. The collection offers a practical roadmap for integrating technical tools into peacebuilding to foster resilient societies and non-violent conflict transformations.

The aim of this study was to describe how the detection of protozoan and helminth parasites has been affected by the introduction of polymerase chain reaction (PCR) and changes in test algorithms. We extracted data about faecal samples tested for parasites (n = 114839) at five Norwegian clinical microbiology laboratories. Samples were classified into prePCR or postPCR depending on whether they were submitted before or after the introduction of PCR, and into diagnostic episodes (n = 99320). The number of diagnostic episodes increased 3.7-fold from prePCR to postPCR. Giardia positive episodes doubled, the positivity rate decreased from 2.0% to 1.3%. Cryptosporidium was hardly detected prePCR and increased to a positivity rate of 1.2%. Entamoeba histolytica was rarely found. Episodes examined for helminths decreased 51%, the number of positive episodes decreased 34%. Samples from immigrants were more likely to be positive for Giardia, E. histolytica, or helminths and less likely to be Cryptosporidium positive. During the COVID-19 pandemic, the number of Giardia and helminth-positive episodes decreased. Cryptosporidium-positive episodes remained unchanged. The implementation of multiplex PCR for protozoa led to a doubling of Giardia cases and a better test for Cryptosporidium. Fewer microscopy examinations raise concerns that helminth infections may be overlooked.

A random temporal graph is an Erdős-Rényi random graph $G(n,p)$, together with a random ordering of its edges. A path in the graph is called increasing if the edges on the path appear in increasing order. A set $S$ of vertices forms a temporal clique if for all $u,v \in S$, there is an increasing path from $u$ to $v$. Becker, Casteigts, Crescenzi, Kodric, Renken, Raskin and Zamaraev [(2023) Giant components in random temporal graphs. arXiv,2205.14888] proved that if $p=c\log n/n$ for $c\gt 1$, then, with high probability, there is a temporal clique of size $n-o(n)$. On the other hand, for $c\lt 1$, with high probability, the largest temporal clique is of size $o(n)$. In this note, we improve the latter bound by showing that, for $c\lt 1$, the largest temporal clique is of constant size with high probability.

The digital twin approach has gained recognition as a promising solution to the challenges faced by the Architecture, Engineering, Construction, Operations, and Management (AECOM) industries. However, its broader application across some AECOM sectors remains limited. A significant obstacle is that traditional DTs rely on deterministic models, which require deterministic input parameters. This limits their accuracy, as they do not account for the substantial uncertainties that are inherent in AECOM projects. These uncertainties are particularly pronounced in geotechnical design and construction. To address this challenge, we propose a probabilistic digital twin (PDT) framework that extends traditional DT methodologies by incorporating uncertainties and is tailored to the requirements of geotechnical design and construction. The PDT framework provides a structured approach to integrating all sources of uncertainty, including aleatoric, data, model, and prediction uncertainties, and propagates them throughout the entire modeling process. To ensure that site-specific conditions are accurately reflected as additional information is obtained, the PDT leverages Bayesian methods for model updating. The effectiveness of the PDT framework is showcased through an application to a highway foundation construction project, demonstrating its potential to integrate existing probabilistic methods to improve decision-making and project outcomes in the face of significant uncertainties. By embedding these methods within the PDT framework, we lower the barriers to practical implementation, making probabilistic approaches more accessible and applicable in real-world engineering workflows.

This paper documents the details of the design, verification, and certification of a novel technology: a remote monitoring system (digital twin) for a voyage data recorder, referred to as the HermAce Gateway. The electronic components, data transfer, and storage principle explain how the HermAce Gateway communicates and records safety-critical messages. Various prospective benefits to the industry are provided, primarily regarding the opportunities for remote support and testing that the digital twin facilitates. The HermAce Gateway was independently verified through a combination of semi-automated software in the loop and selected complimentary hardware in the loop tests. Different types of communication were simulated in multiple ways, including approximating real-world scenarios. Alarms contained in correctly formed messages were found to be detected and recorded by the HermAce Gateway, and a discussion of how this evidence can be quantified in the context of reducing uncertainty in the reliability of a digital twin. Certification of a digital system is a new concept in the maritime industry. The identification of functional requirements, which informed the verification testing, and the development of an AI register for what is expected to be an increasing number of such systems are also documented.

This article examines the impact of generative artificial intelligence (GAI) on higher education, emphasizing its effects in the broader educational contexts. As AI continues to reshape the landscape of teaching and learning, it is imperative for higher education institutions to adapt rapidly to equip graduates for the challenges of a progressively automated global workforce. However, a critical question emerges: will GAI lead to a more inclusive future of learning, or will it deepen existing divides and create a future where educational access and success are increasingly unequal? This study employs both theoretical and empirical approaches to explore the transformative potential of GAI. Drawing upon the literature on AI and education, we establish a framework that categorizes the essential knowledge and skills needed by graduates in the GAI era. This framework includes four key capability sets: AI ethics, AI literacy (focusing on human-replacement technologies), human–AI collaboration (emphasizing human augmentation), and human-distinctive capacities (highlighting unique human intelligence). Our empirical analysis involves scrutinizing GAI policy documents and the core curricula mandated for all graduates across leading Asian universities. Contrary to expectations of a uniform AI-driven educational transformation, our findings expose significant disparities in AI readiness and implementation among these institutions. These disparities, shaped by national and institutional specifics, are likely to exacerbate existing inequalities in educational outcomes, leading to divergent futures for individuals and universities alike in the age of GAI. Thus, this article not only maps the current landscape but also forecasts the widening educational gaps that GAI might engender.

During pregnancy, colonization by genital mycoplasmas may be associated with adverse outcomes. This study was conducted to investigate the prevalence of four species of Mollicutes (Mycoplasma hominis, Mycoplasma genitalium, Ureaplasma parvum, and Ureaplasma urealyticum) in pregnant women receiving high-risk prenatal care and to evaluate possible associated factors. Data collection included the application of a questionnaire and the collection of cervical swabs from pregnant women. Species identification was performed by real-time PCR. The overall prevalence of Mollicutes was 60.97%. 55.9% of pregnant women were colonized by Ureaplasma spp., and 19.51% by Mycoplasma spp. The prevalence rates by species were 48.78% for U. parvum, 11.59% for U. urealyticum, 18.9% for M. hominis, and 1.22% for M. genitalium. Age, 12 years of schooling or more, age at first sexual intercourse up to 14 years, third trimester of pregnancy, having undergone infertility treatment, presence of STI, and groin lymph nodes were associated with a higher prevalence of microorganisms. The results presented are of utmost importance for understanding the prevalence of these microorganisms, the characteristics of colonized pregnant women, and planning screening strategies and interventions that minimize the negative impacts of these infections.

In 1987, the United Nations Brundtland Commission defined sustainability as “meeting the needs of the present without compromising the ability of future generations to meet their own needs.” In recent years, the sustainability agenda has grown in importance, with many countries, regulators, industries shifting to implement sustainable practices. For retirement funds this means providing a lasting income in retirement for members, whilst ensuring a positive contribution to society and the environment. Retirement funds, with long-term liabilities, are therefore well placed and can play a significant role in contributing to the overall objective. This paper explores how retirement funds in various countries are progressing this agenda. We then introduce a sustainability reporting index, which measures the breadth and quality of how retirement funds can report on pricing in social and environmental externalities in the provision of a pension promise. The sustainability reporting index includes the financial inclusion aspects of retirement funds as well as how social and environmental externalities can be factored into the running of a fund and how its assets are invested. It explores the key areas that need to be monitored, the types of data required and the types of analytics that can be used by various stakeholders. The sustainability reporting index is intended to provide a benchmark against which various stakeholders can measure the effectiveness of their approach in pricing in these externalities. Actuaries of retirement funds can use the framework to go beyond focussing purely on the financial aspects of a fund, incorporating material non-financial aspects to ensure the provision of a sustainable pension income.

Invasive Group B Streptococcus (GBS) infection caused by the highly virulent Sequence Type 283 (ST283) strain has been linked to consumption of raw freshwater fish. In late summer 2024, enhanced surveillance in Hong Kong detected a surge of invasive ST283 cases.

A retrospective case–control study was conducted involving all invasive GBS patients reported during August to September 2024. Data were collected through standardised interviewer-administered questionnaires. Cases were defined as patients infected with the ST283 strain, while controls had non-ST283 strains. A multivariate logistic regression analysis was conducted to determine the risk factors.

Among 170 invasive GBS patients, 131 (77%) were identified as cases and 39 (23%) as controls. Physical handling of raw freshwater fish was found to be the strongest risk factor for ST283 infection (adjusted odds ratio: 8.4, 95% confidence interval: 1.4–50.1).

This study represents the first epidemiological evidence specifically linking physical contact with raw freshwater fish to an increased risk of invasive GBS ST283 infection. Effective interdepartmental coordination, intensive public health education, active surveillance, and prompt environmental interventions effectively mitigated this large outbreak. The findings underscore the need for sustainable preventive strategies targeting high-risk fish handling practices, particularly during warm periods favouring environmental proliferation of ST283.

The present paper develops a unified approach when dealing with short- or long-range dependent processes with finite or infinite variance. We are concerned with the convergence rate in the strong law of large numbers (SLLN). Our main result is a Marcinkiewicz–Zygmund law of large numbers for $S_{n}(f)= \sum_{i=1}^{n}f(X_{i})$, where $\{X_i\}_{i\geq 1}$ is a real stationary Gaussian sequence and $f(\!\cdot\!)$ is a measurable function. Key technical tools in the proofs are new maximal inequalities for partial sums, which may be useful in other problems. Our results are obtained by employing truncation alongside new maximal inequalities. The result can help to differentiate the effects of long memory and heavy tails on the convergence rate for limit theorems.