We consider bond percolation on high-dimensional product graphs $G=\square _{i=1}^tG^{(i)}$, where $\square$ denotes the Cartesian product. We call the $G^{(i)}$ the base graphs and the product graph $G$ the host graph. Very recently, Lichev (J. Graph Theory, 99(4):651–670, 2022) showed that, under a mild requirement on the isoperimetric properties of the base graphs, the component structure of the percolated graph $G_p$ undergoes a phase transition when $p$ is around $\frac{1}{d}$, where $d$ is the average degree of the host graph.

In the supercritical regime, we strengthen Lichev’s result by showing that the giant component is in fact unique, with all other components of order $o(|G|)$, and determining the sharp asymptotic order of the giant. Furthermore, we answer two questions posed by Lichev (J. Graph Theory, 99(4):651–670, 2022): firstly, we provide a construction showing that the requirement of bounded degree is necessary for the likely emergence of a linear order component; secondly, we show that the isoperimetric requirement on the base graphs can be, in fact, super-exponentially small in the dimension. Finally, in the subcritical regime, we give an example showing that in the case of irregular high-dimensional product graphs, there can be a polynomially large component with high probability, very much unlike the quantitative behaviour seen in the Erdős-Rényi random graph and in the percolated hypercube, and in fact in any regular high-dimensional product graphs, as shown by the authors in a companion paper (Percolation on high-dimensional product graphs. arXiv:2209.03722, 2022).

Chickenpox (varicella) is a rare occurrence in healthcare settings in the USA, but can be transmitted to healthcare workers (HCWs) from patients with herpes zoster who, in turn, can potentially transmit it further to unimmunized, immunosuppressed, at-risk, vulnerable patients. It is uncommon due to the inclusion of varicella vaccination in the recommended immunization schedule for children and screening for varicella immunity in HCWs during employment. We present a case report of hospital-acquired chickenpox in a patient who developed the infection during his prolonged hospital stay through a HCW who had contracted chickenpox after exposure to our patient’s roommate with herpes zoster. There was no physical contact between the roommates, but both patients had a common HCW as caregiver. The herpes zoster patient was placed in airborne precautions immediately, but the HCW continued to work and have physical contact with our patient. The HCW initially developed chickenpox 18 days after exposure to the patient with herpes zoster, and our patient developed chickenpox 17 days after the HCW. The timeline and two incubation periods, prior to our patient developing chickenpox, indicate transmission of chickenpox in the HCW from exposure to the herpes zoster patient and subsequently to our patient. The case highlights the potential for nosocomial transmission of chickenpox (varicella) to unimmunized HCWs from exposure to patients with herpes zoster and further transmission to unimmunized patients. Verification of the immunization status of HCWs at the time of employment, mandating immunity, furloughing unimmunized staff after exposure to herpes zoster, and postexposure prophylaxis with vaccination or varicella zoster immunoglobulin (Varizig) will minimize the risk of transmission of communicable diseases like chickenpox in healthcare settings. Additionally, establishing patients’ immunity, heightened vigilance and early identification of herpes zoster in hospitalized patients, and initiation of appropriate infection control immediately will further prevent such occurrences and improve patient safety.

We investigate the reengineeering of interbank networks with a specific focus on capital increase. We consider a scenario where all other components of the network’s infrastructure remain stable (a practical assumption for short-term situations). Our objective is to assess the impact of raising capital on the network’s robustness and to address the following key aspects. First, given a predefined target for network robustness, our aim is to achieve this goal optimally, minimizing the required capital increase. Second, in cases where a total capital increase has been determined, the central challenge lies in distributing this increase among the banks in a manner that maximizes the stability of the network. To tackle these challenges, we begin by developing a comprehensive theoretical framework. Subsequently, we formulate an optimization model for the network’s redesign. Finally, we apply this framework to practical examples, highlighting its applicability in real-world scenarios.

We aimed to assess the burden and trend of the HIV/AIDS epidemic among older adults over the past three decades at different geographical levels, based on the data collected from the Global Burden of Diseases (GBD) study 2019. This assessment identified the average annual percentage changes (AAPCs) using Joinpoint regression analysis. Globally, the incidence of HIV/AIDS has decreased (AAPC = −3.107); however, the overall prevalence has consistently increased (AAPC = 5.557). Additionally, both mortality (AAPC = 2.166) and disability-adjusted life years (DALYs; AAPC = 2.429) have increased. The highest increasing trends in female HIV/AIDS incidence and prevalence were observed in the Central Asia region. However, for males, these trends were observed in the Oceania region and the high-income Asia Pacific region, respectively. In recent decades, females aged 70–74 years had the highest incidence and prevalence, while males aged 70–74 years had highest mortality and DALYs in low social development index (SDI) regions. Unsafe sex resulted in 15 381.16 deaths, accounting for 90.73% of all HIV/AIDS deaths, and 331 140.56 DALYs, accounting for 91.12% of all HIV/AIDS DALYs. The HIV/AIDS disease burden differs by region, age, and sex among older adults. Sexual health education and targeted screening for older adults are recommended.

The devastating effects of the coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may not end when the acute illness has terminated. A subset of COVID-19 patients may have symptoms that persist for months. This condition has been described as ‘long COVID’. From a historical perspective, it has been recognized that serious long-term neurological sequelae have been associated with RNA viruses such as influenza viruses and coronaviruses. A potential intervention for early post-COVID-19 neuropsychiatric impairment may be the commonly employed, readily available, reasonably priced macrolide antibiotic, azithromycin. We have observed a favourable clinical response with azithromycin in three patients with neurological symptoms associated with long COVID-19. We recommend considering formal clinical trials using azithromycin for patients with post-COVID-19 infection neurological changes including ‘COVID fog’ or the more severe neurological symptoms that may later develop.

Graphical models with heavy-tailed factors can be used to model extremal dependence or causality between extreme events. In a Bayesian network, variables are recursively defined in terms of their parents according to a directed acyclic graph (DAG). We focus on max-linear graphical models with respect to a special type of graph, which we call a tree of transitive tournaments. The latter is a block graph combining in a tree-like structure a finite number of transitive tournaments, each of which is a DAG in which every two nodes are connected. We study the limit of the joint tails of the max-linear model conditionally on the event that a given variable exceeds a high threshold. Under a suitable condition, the limiting distribution involves the factorization into independent increments along the shortest trail between two variables, thereby imitating the behaviour of a Markov random field.

We are also interested in the identifiability of the model parameters in the case when some variables are latent and only a subvector is observed. It turns out that the parameters are identifiable under a criterion on the nodes carrying the latent variables which is easy and quick to check.

We study three classes of shock models governed by an inverse gamma mixed Poisson process (IGMP), namely a mixed Poisson process with an inverse gamma mixing distribution. In particular, we analyze (1) the extreme shock model, (2) the δ-shock model, and the (3) cumulative shock model. For the latter, we assume a constant and an exponentially distributed random threshold and consider different choices for the distribution of the amount of damage caused by a single shock. For all the treated cases, we obtain the survival function, together with the expected value and the variance of the failure time. Some properties of the inverse gamma mixed Poisson process are also disclosed.

Understanding the spatio-temporal patterns of users’ travel behavior on public transport (PT) systems is essential for more assertive transit planning. With this in mind, the aim of this article is to diagnose the spatial and temporal travel patterns of users of Fortaleza’s PT network, which is a trunk-feeder network whose fares are charged by a tap-on system. To this end, 20 databases were used, including global positioning system, user registration, and PT smart card data from November 2018, prior to the pandemic. The data set was processed and organized into a database with a relational model and an Extraction, Transformation, and Loading process. A data mining approach based on Machine Learning models was applied to evaluate travel patterns. As a result, it was observed that users’ first daily use has a higher percentage of spatial and temporal patterns when compared to their last daily use. In addition, users rarely show spatial and temporal patterns at the same time.

In recent years, and with the COVID disruption, many companies have moved toward digitization, adopting digital supply chains for enhanced efficiency. This coincided with the Western Governments mandating, through modern slavery legislation, that multinational companies should mitigate human rights risks in their supply chains. In addition, the Indian government has been making major efforts to equip residents in India with digital identities; first with the Aadhaar identity system, and, on August 26, 2021, the eShram portal aimed specifically at registering informal workers recognizing them formally as part of the Indian labour force. This article shows how a full digitization of the supply chains might be problematic, and in the extreme, might threaten the livelihoods of homeworkers. For the homeworkers to survive the seemingly inevitable digitization, there is a clear need to ensure that they have a direct representation in the digital supply chains. Given the limited ability of the homeworkers to directly represent themselves, we need appropriate models of digital custodianship and policies for promoting their uptake. We discuss the shape that such solutions might take. Finally, an open acceptance by brands of homeworking as a part of their supply chains is called for, paving the way to a public acceptance of these workers’ right to a minimum/living wage. To engineer widespread acceptance is an insurmountable task. It is hoped that the eShram scheme will help to change the political balance in India as the informal workers now become “traceable.”

Detection of defects and identification of symptoms in monitoring industrial systems is a widely studied problem with applications in a wide range of domains. Most of the monitored information extracted from systems corresponds to data series (or time series), where the evolution of values through one or multiple dimensions directly illustrates its health state. Thus, an automatic anomaly detection method in data series becomes crucial. In this article, we propose a novel method based on a convolutional neural network to detect precursors of anomalies in multivariate data series. Our contribution is twofold: We first describe a new convolutional architecture dedicated to multivariate data series classification; We then propose a novel method that returns dCAM, a dimension-wise Class Activation Map specifically designed for multivariate time series that can be used to identify precursors when used for classifying normal and abnormal data series. Experiments with several synthetic datasets demonstrate that dCAM is more accurate than previous classification approaches and a viable solution for discriminant feature discovery and classification explanation in multivariate time series. We then experimentally evaluate our approach on a real and challenging use case dedicated to identifying vibration precursors on pumps in nuclear power plants.

The Global Financial Crisis of 2007–2008 has elicited various debates, ranging from ethics over the stability of the banking system to subtle technical issues regarding the Gaussian and other copulas. We want to look at the crisis from a particular perspective. Credit derivatives have much in common with treaty reinsurance, including risk transfer via pooling and layering, scarce data, skewed distributions, and a limited number of specialised players in the market. This leads to a special mixture of mathematical/statistical and behavioural challenges. Reinsurers have been struggling to cope with these, not always successfully, but they have learned some lessons over the course of more than one century in business. This has led to certain rules being adopted by the reinsurance market and to a certain mindset being adopted by the individuals working in the industry. Some cultures established in the reinsurance world could possibly inspire markets like the credit derivatives market, but the subtle differences between the two worlds matter. We will see that traditional reinsurance has built-in incentives for (some) fairness, while securitisation can foster opportunism.

Self-sovereign identity (SSI) is an emerging and promising concept that enables users to control their identity while enhancing security and privacy compared to other identity management (IDM) approaches. Despite the recent advancements in SSI technologies, federated identity management (FIDM) systems continue to dominate the IDM market. Selecting an IDM to implement for a specific application is a complex task that requires a thorough understanding of the potential external cyber risks. However, existing research scarcely compares SSI and FIDM from the perspective of these external threats. In response to this gap, our article provides an attack surface analysis focused solely on external threats for both systems. This analysis can serve as a reference to compare the relevant security and privacy risks associated with these external threats. The threat landscapes of external attackers were systematically synthesized from the main components and functionalities of the common standards and designs. We further present a use case analysis that applies this attack surface analysis to compare the external cyber risks of the two systems in detail when managing cross-border identity between European countries. This work can be particularly useful for considering a more secure design for future IDM applications, taking into account the landscape of external threats.

We carried out a retrospective study of acute gastroenteritis (AGE) outbreaks reported between 1 January 2015 and 31 December 2021 in Catalonia (Spain) to compare the incidence from 2015 to 2019 with that observed from 2020 to 2021. We observed a higher incidence rate of outbreaks during the prepandemic period (16.89 outbreaks/1,000,000 person-years) than during the pandemic period (6.96 outbreaks/1,000,000 person-years) (rate ratio (RR) 0.41; 95% confidence interval (CI) 0.34 to 0.51). According to the aetiology of the outbreak, those of viral aetiology decreased from 7.82 to 3.38 outbreaks/1,000,000 person-years (RR 2.31; 95% CI 1.72 to 3.12), and those of bacterial aetiology decreased from 5.01 to 2.78 outbreaks/1,000,000 person-years (RR 1.80; 95% CI 1.29 to 2.52). There was a great reduction in AGE outbreaks in Catalonia. This reduction may have been due to the effect of the nonpharmaceutical measures applied to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the collapse of the healthcare system and epidemiological surveillance services may also have had a strong influence.

Since 1998, Bogotá has consistently made substantial efforts to foster the bicycle’s role as a primary mode of transportation. Recent years have witnessed a compelling aspiration for the city to ascend as the “bicycle capital of the world,” evident in its accomplishment of 6.6% of daily trips completed by bicycle in 2019. This achievement translates to 880.367 daily cycling journeys (District Secretariat of Mobility of Bogotá, 2019). These statistics surpass regional benchmarks; for instance, other capital cities such as Santiago de Chile account for 510.569 bicycle trips, Mexico City for 433.981, and Rio de Janeiro for 217.000 (Ríos et al., 2015). Despite this progress, Bogotá lacks a comprehensive evaluation of both infrastructure quality and the user experience while cycling.

This translational research article aimed to explore this gap by delving into the integration of user perceptions and experiences within the policy formulation process. This strategic approach is poised to enhance cycling’s allure as a mode of transportation for prospective cyclists while simultaneously maximizing the efficiency of investments in cycling infrastructure.