This paper analyzes single-item continuous-review inventory models with random supplies in which the inventory dynamic between orders is described by a diffusion process, and a long-term average cost criterion is used to evaluate decisions. The models in this class have general drift and diffusion coefficients and boundary points that are consistent with the notion that demand should tend to reduce the inventory level. Random yield is described by a (probability) transition function which depends on the inventory on hand and the nominal amount ordered; it is assumed to be a distribution with support in the interval determined by the order-from and the nominal order-to locations of the stock level. Using weak convergence arguments involving average expected occupation and ordering measures, conditions are given for the optimality of an (s, S) ordering policy in the general class of policies with finite expected cost. The characterization of the cost of an (s, S) policy as a function of two variables naturally leads to a nonlinear optimization problem over the stock levels s and S, and the existence of an optimizing pair $(s^*,S^*)$ is established under weak conditions. Thus, optimal policies of inventory models with random supplies can (easily) be numerically computed. The range of applicability of the optimality result is illustrated on several inventory models with random yields.

Sexually transmitted infections caused by Chlamydia trachomatis (Ct) and Mycoplasma genitalium (Mg) have significant implications both at the individual and societal levels. Our study evaluated various co-factors associated with persistent serum IgG-antibodies to Ct and Mg. Three hundred and twenty nine pregnant women and 135 men from the Finnish Family HPV study were analyzed for serum IgG-antibodies of pGP3 for Ct and MgPa and rMgPa for Mg using multiplex serology. Seropersistence to both Ct and Mg was more common in women (30.4% and 13.3%) than in men (17.4% and 5.3%). The number of lifetime sexual partners above 10, the practice of anal sex, and a history of diagnosed Ct were associated with seropersistence to Ct in women, adjusted ORs 5.6 (95%CI 1.39–22.29), 15.3 (95%CI 1.18–197.12) and 18.0 (95%CI 5.59–57.92), respectively. The increasing number of partners before the age of 20 was the main risk factor for seropersistence among women with Mg, adjusted OR range from 5.0 to 12.3 (95%CI range 1.17–100.90) and in men only with 6–10 partners for Ct, adjusted OR 12.6 (95%CI 1.55–102.49). To conclude, persistent Ct antibodies were associated with various sexual activities, and Mg seropositivity was mainly associated with increased sexual activity in early adulthood.

We study the last exit time that a spectrally negative Lévy process is below zero until it reaches a positive level b, denoted by $g_{\tau_b^+}$. We generalize the results of the infinite-horizon last exit time explored by Chiu and Yin (2005) by incorporating a random horizon $\tau_b^+$, which represents the first passage time above b. We derive an explicit expression for the joint Laplace transform of $g_{\tau_b^+}$ and $\tau_b^+$ by utilizing a hybrid observation scheme approach proposed by Li, Willmot, and Wong (2018). We further study the optimal prediction of $g_{\tau_b^+}$ in the $L_1$ sense, and find that the optimal stopping time is the first passage time above a level $y_b^{\ast}$, with an explicit characterization of the stopping boundary $y_b^{\ast}$. As examples, Brownian motion with drift and the Cramér–Lundberg model with exponential jumps are considered.

An estimated 129000 cases of Lyme borreliosis (LB) are reported annually in Europe. In 2022, we conducted a representative web-based survey of 28034 persons aged 18–65 years old in 20 European countries to describe tick and LB risk exposures and perceptions. Nearly all respondents (95.0%) were aware of ticks (range, 90.4% in the UK to 98.8% in Estonia). Among those aware of ticks, most (85.1%) were also aware of LB (range, 70.3% in Switzerland to 97.0% in Lithuania). Overall, 8.3% of respondents reported a past LB diagnosis (range, 3.0% in Romania to 13.8% in Sweden). Respondents spent a weekly median of 7 (interquartile range [IQR] 3–14) hours in green spaces at home and 9 (IQR 4–16) hours away from home during April–November. The most common tick prevention measures always or often used were checking for ticks (44.8%) and wearing protective clothing (40.2%). This large multicountry survey provided needed data that can be used to design targeted LB prevention programmes in Europe.

We studied severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and vaccination status among six ethnic groups in Amsterdam, the Netherlands. We analysed participants of the Healthy Life in an Urban Setting cohort who were tested for SARS-CoV-2 spike protein antibodies between 17 May and 21 November 2022. We categorized participants with antibodies as only infected, only vaccinated (≥1 dose), or both infected and vaccinated, based on self-reported prior infection and vaccination status and previous seroprevalence data. We compared infection and vaccination status between ethnic groups using multivariable, multinomial logistic regression. Of the 1,482 included participants, 98.5% had SARS-CoV-2 antibodies (P between ethnic groups = 0.899). Being previously infected and vaccinated ranged from 36.2% (95% confidence interval (CI) = 28.3–44.1%) in the African Surinamese to 64.5% (95% CI = 52.9–76.1%) in the Ghanaian group. Compared to participants of Dutch origin, participants of South-Asian Surinamese (adjusted odds ratio (aOR) = 6.74, 95% CI = 2.61–17.45)), African Surinamese (aOR = 23.32, 95% CI = 10.55–51.54), Turkish (aOR = 8.50, 95% CI = 3.05–23.68), or Moroccan (aOR = 22.33, 95% CI = 9.48–52.60) origin were more likely to be only infected than infected and vaccinated, after adjusting for age, sex, household size, trust in the government’s response to the pandemic, and month of study visit. SARS-CoV-2 infection and vaccination status varied across ethnic groups, particularly regarding non-vaccination. As hybrid immunity is most protective against coronavirus disease 2019, future vaccination campaigns should encourage vaccination uptake in specific demographic groups with only infection.

For each uniformity $k \geq 3$, we construct $k$ uniform linear hypergraphs $G$ with arbitrarily large maximum degree $\Delta$ whose independence polynomial $Z_G$ has a zero $\lambda$ with $\left \vert \lambda \right \vert = O\left (\frac {\log \Delta }{\Delta }\right )$. This disproves a recent conjecture of Galvin, McKinley, Perkins, Sarantis, and Tetali.

Leptospira are bacteria that cause leptospirosis in both humans and animals. Human Leptospira infections in Uganda are suspected to arise from animal–human interactions. From a nationwide survey to determine Leptospira prevalence and circulating sequence types in Uganda, we tested 2030 livestock kidney samples, and 117 small mammals (rodents and shrews) using real-time PCR targeting the lipL32 gene. Pathogenic Leptospira species were detected in 45 livestock samples but not in the small mammals. The prevalence was 6.12% in sheep, 4.25% in cattle, 2.08% in goats, and 0.46% in pigs. Sequence typing revealed that Leptospira borgpetersenii, Leptospira kirschneri, and Leptospira interrogans are widespread across Uganda, with 13 novel sequence types identified. These findings enhance the East African MLST database and support the hypothesis that domesticated animals may be a source of human leptospirosis in Uganda, highlighting the need for increased awareness among those in close contact with livestock.

In July 2022, a genetically linked and geographically dispersed cluster of 12 cases of Shiga toxin-producing Escherichia coli (STEC) O103:H2 was detected by the UK Health Security Agency using whole genome sequencing. Review of food history questionnaires identified cheese (particularly an unpasteurized brie-style cheese) and mixed salad leaves as potential vehicles. A case–control study was conducted to investigate exposure to these products. Case food history information was collected by telephone. Controls were recruited using a market research panel and self-completed an online questionnaire. Univariable and multivariable analyses were undertaken using Firth Logistic Regression. Eleven cases and 24 controls were included in the analysis. Consumption of the brie-style cheese of interest was associated with illness (OR 57.5, 95% confidence interval: 3.10–1,060). Concurrently, the production of the brie-style cheese was investigated. Microbiological sample results for the cheese products and implicated dairy herd did not identify the outbreak strain, but did identify the presence of stx genes and STEC, respectively. Together, epidemiological, microbiological, and environmental investigations provided evidence that the brie-style cheese was the vehicle for this outbreak. Production of unpasteurized dairy products was suspended by the business operator, and a review of practices was performed.

We discuss the emerging technology of digital twins (DTs) and the expected demands as they scale to represent increasingly complex, interconnected systems. Several examples are presented to illustrate core use cases, highlighting a progression to represent both natural and engineered systems. The forthcoming challenges are discussed around a hierarchy of scales, which recognises systems of increasing aggregation. Broad implications are discussed, encompassing sensing, modelling, and deployment, alongside ethical and privacy concerns. Importantly, we endorse a modular and peer-to-peer view for aggregate (interconnected) DTs. This mindset emphasises that DT complexity emerges from the framework of connections (Wagg et al. [2024, The philosophical foundations of digital twinning, Preprint]) as well as the (interpretable) units that constitute the whole.

Reliability analysis of stress–strength models usually assumes that the stress and strength variables are independent. However, in numerous real-world scenarios, stress and strength variables exhibit dependence. This paper investigates the reliability estimation in a multicomponent stress–strength model for parallel-series system assuming that the dependence between stress and strength is based on the Clayton copula. The estimators for the unknown parameters and system reliability are derived using the two-step maximum likelihood estimation and the maximum product spacing methods. Additionally, confidence intervals are constructed by utilizing asymptotically normal distribution theory and bootstrap method. Furthermore, Monte Carlo simulations are conducted to compare the effectiveness of the proposed inference methods. Finally, a real dataset is analyzed for illustrative purposes.

In this paper, the model of bisexual branching processes affected by viral infectivity and with random control functions in independent and identically distributed (i.i.d.) random environments is established and the Markov property is given firstly. Then the relations of the probability generating functions of this model are studied, and some sufficient conditions for process extinction under common mating functions are presented. Finally, the limiting behaviors of the considered model after proper normalization, such as the sufficient conditions for the convergence in L1 and L2 and almost everywhere convergence, are investigated under the condition that the random control functions are super additive.

Multiple osteoarticular tuberculosis (MOT) represents an uncommon yet severe form of tuberculosis, characterized by a lack of systematic analysis and comprehension. Our objective was to delineate MOT’s epidemiological characteristics and establish a scientific foundation for prevention and treatment. We conducted searches across eight databases to identify relevant articles. Pearson’s chi-square test (Fisher’s exact test) and Bonferroni method were employed to assess osteoarticular involvement among patients of varying age and gender (α = 0.05). The study comprised 98 articles, encompassing 151 cases from 22 countries, with China and India collectively contributing 67.55% of cases. MOT predominantly affected individuals aged 0–30 years (58.94%). Pulmonary tuberculosis was evident in 16.55% of cases, with spinal involvement prevalent (57.62%). Significant differences were noted in trunk, spine, thoracic, and lumbar vertebrae involvement, as well as type I lesions across age groups, increasing with age. Moreover, significant differences were observed in upper limb bone involvement and type II lesions across age groups, decreasing with age. Gender differences were not significant. MOT primarily manifests in China and India, predominantly among younger individuals, indicating age-related variations in osteoarticular involvement. Enhanced clinical awareness is crucial for accurate MOT diagnosis, mitigating missed diagnoses and misdiagnoses.

Cause-of-death mortality forecasting, a key topic in public health and actuarial science, is a challenging task due to the difficulty of modeling that accounts for dependencies among causes of death. While several cause-of-death mortality models have been proposed to address this difficulty, little attention has been paid to improving their predictive performance. Recently, purely data-driven approaches using tensor decomposition methods have been introduced to cause-of-death mortality modeling, demonstrating strong out-of-sample predictive performance compared to existing models. However, these methods have difficulties in the interpretability of multi-rank tensor components to achieve strong predictive performance. In response, we propose a novel tensor-based cause-of-death mortality model by replacing the tensor decomposition with a convolutional autoencoder with a one-dimensional latent layer that provides a Lee-Carter-like time-series factor; the model also provides the age sensitivity of cause-specific log mortality to the time-series factor. Due to the representational capability of the neural network, our model achieves better predictive performance compared to the existing tensor decomposition-based models, despite the simplified latent layer for model interpretability.

Vibration-based structural health monitoring (SHM) of (large) infrastructure through operational modal analysis (OMA) is a commonly adopted strategy. This is typically a four-step process, comprising estimation, tracking, data normalization, and decision-making. These steps are essential to ensure structural modes are correctly identified, and results are normalized for environmental and operational variability (EOV). Other challenges, such as nonstructural modes in the OMA, for example, rotor harmonics in (offshore) wind turbines (OWTs), further complicate the process. Typically, these four steps are considered independently, making the method simple and robust, but rather limited in challenging applications, such as OWTs. Therefore, this study aims to combine tracking, data normalization, and decision-making through a single machine learning (ML) model. The presented SHM framework starts by identifying a “healthy” training dataset, representative of all relevant EOV, for all structural modes. Subsequently, operational and weather data are used for feature selection and a comparative analysis of ML models, leading to the selection of tree-based learners for natural frequency prediction. Uncertainty quantification (UQ) is introduced to identify out-of-distribution instances, crucial to guarantee low modeling error and ensure only high-fidelity structural modes are tracked. This study uses virtual ensembles for UQ through the variance between multiple truncated submodel predictions. Practical application to monopile-supported OWT data demonstrates the tracking abilities, separating structural modes from rotor dynamics. Control charts show improved decision-making compared to traditional reference-based methods. A synthetic dataset further confirms the approach’s robustness in identifying relevant natural frequency shifts. This study presents a comprehensive data-driven approach for vibration-based SHM.

Let $\mathbb{P}_\kappa(n)$ be the probability that n points $z_1,\ldots,z_n$ picked uniformly and independently in $\mathfrak{C}_\kappa$, a regular $\kappa$-gon with area 1, are in convex position, that is, form the vertex set of a convex polygon. In this paper, we compute $\mathbb{P}_\kappa(n)$ up to asymptotic equivalence, as $n\to+\infty$, for all $\kappa\geq 3$, which improves on a famous result of Bárány (Ann. Prob. 27, 1999). The second purpose of this paper is to establish a limit theorem which describes the fluctuations around the limit shape of an n-tuple of points in convex position when $n\to+\infty$. Finally, we give an asymptotically exact algorithm for the random generation of $z_1,\ldots,z_n$, conditioned to be in convex position in $\mathfrak{C}_\kappa$.

Understanding and forecasting mortality by cause is an essential branch of actuarial science, with wide-ranging implications for decision-makers in public policy and industry. To accurately capture trends in cause-specific mortality, it is critical to consider dependencies between causes of death and produce forecasts by age and cause coherent with aggregate mortality forecasts. One way to achieve these aims is to model cause-specific deaths using compositional data analysis (CODA), treating the density of deaths by age and cause as a set of dependent, nonnegative values that sum to one. A major drawback of standard CODA methods is the challenge of zero values, which frequently occur in cause-of-death mortality modeling. Thus, we propose using a compositional power transformation, the $\alpha$-transformation, to model cause-specific life-table death counts. The $\alpha$-transformation offers a statistically rigorous approach to handling zero value subgroups in CODA compared to ad hoc techniques: adding an arbitrarily small amount. We illustrate the $\alpha$-transformation in England and Wales and US death counts by cause from the Human Cause-of-Death database, for cardiovascular-related causes of death. The results demonstrate the $\alpha$-transformation improves forecast accuracy of cause-specific life-table death counts compared with log-ratio-based CODA transformations. The forecasts suggest declines in the proportions of deaths from major cardiovascular causes (myocardial infarction and other ischemic heart diseases).

Klebsiella pneumoniae are opportunistic pathogens which can cause mastitis in dairy cattle. K. pneumoniae mastitis often has a poor cure rate and can lead to the development of chronic infection, which has an impact on both health and production. However, there are few studies which aim to fully characterize K. pneumoniae by whole-genome sequencing from bovine mastitis cases. Here, K. pneumoniae isolates associated with mastitis in dairy cattle were identified using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and whole-genome sequencing. Furthermore, whole-genome sequence data were used for phylogenetic analyses and both virulence and antimicrobial resistance (AMR) prediction, in parallel with phenotypic AMR testing. Forty-two isolates identified as K. pneumoniae were subject to whole-genome sequencing, with 31 multi-locus sequence types being observed, suggesting the source of these isolates was likely environmental. Isolates were examined for key virulence determinants encoding acquired siderophores, colibactin, and hypermucoidy. The majority of these were absent, except for ybST (encoding yersiniabactin) which was present in six isolates. Across the dataset, there were notable levels of phenotypic AMR against streptomycin (26.2%) and tetracycline (19%), and intermediate susceptibility to cephalexin (26.2%) and neomycin (21.4%). Of importance was the detection of two ESBL-producing isolates, which demonstrated multi-drug resistance to amoxicillin-clavulanic acid, streptomycin, tetracycline, cefotaxime, cephalexin, and cefquinome.

Displacement continues to increase at a global scale and is increasingly happening in complex, multicrisis settings, leading to more complex and deeper humanitarian needs. Humanitarian needs are therefore increasingly outgrowing the available humanitarian funding. Thus, responding to vulnerabilities before disaster strikes is crucial but anticipatory action is contingent on the ability to accurately forecast what will happen in the future. Forecasting and contingency planning are not new in the humanitarian sector, where scenario-building continues to be an exercise conducted in most humanitarian operations to strategically plan for coming events. However, the accuracy of these exercises remains limited. To address this challenge and work with the objective of providing the humanitarian sector with more accurate forecasts to enhance the protection of vulnerable groups, the Danish Refugee Council has already developed several machine learning models. The Anticipatory Humanitarian Action for Displacement uses machine learning to forecast displacement in subdistricts in the Liptako-Gourma region in Sahel, covering Burkina Faso, Mali, and Niger. The model is mainly built on data related to conflict, food insecurity, vegetation health, and the prevalence of underweight to forecast displacement. In this article, we will detail how the model works, the accuracy and limitations of the model, and how we are translating the forecasts into action by using them for anticipatory action in South Sudan and Burkina Faso, including concrete examples of activities that can be implemented ahead of displacement in the place of origin, along routes and in place of destination.