Log-concavity of a joint survival function is proposed as a model for bivariate increasing failure rate (BIFR) distributions. Its connections with or distinctness from other notions of BIFR are discussed. A necessary and sufficient condition for a bivariate survival function to be log-concave (BIFR-LCC) is given that elucidates the impact of dependence between lifetimes on ageing. Illustrative examples are provided to explain BIFR-LCC for both positive and negative dependence.

Theoretical results of frequentist model averaging mainly focus on asymptotic optimality and asymptotic distribution of the model averaging estimator. However, even for basic least squares model averaging, many theoretical problems have not been well addressed yet. This article discusses asymptotic properties of a class of least squares model averaging methods with nested candidate models that includes the Mallows model averaging (MMA) of Hansen (2007, Econometrica 75, 1175–1189) as a special case. Two scenarios are considered: (i) all candidate models are under-fitted; and (ii) the true model is included in the candidate models. We find that in the first scenario, the least squares model averaging method asymptotically assigns weight one to the largest candidate model and the resulting model averaging estimator is asymptotically normal. In the second scenario with a slightly special weight space, if the penalty factor in the weight selection criterion is diverging with certain order, the model averaging estimator is asymptotically optimal by putting weight one to the true model. However, MMA with fixed model dimensions is not asymptotically optimal since it puts nonnegligible weights to over-fitted models. The theoretical results are clearly summarized with their restrictions, and some critical implications are discussed. Monte Carlo simulations confirm our theoretical results.

Asymptotics deviation probabilities of the sum $S_n=X_1+\dots+X_n$ of independent and identically distributed real-valued random variables have been extensively investigated, in particular when $X_1$ is not exponentially integrable. For instance, Nagaev (1969a, 1969b) formulated exact asymptotics results for $\mathbb{P}(S_n>x_n)$ with $x_n\to \infty$ when $X_1$ has a semiexponential distribution. In the same setting, Brosset et al. (2020) derived deviation results at logarithmic scale with shorter proofs relying on classical tools of large-deviation theory and making the rate function at the transition explicit. In this paper we exhibit the same asymptotic behavior for triangular arrays of semiexponentially distributed random variables.

Bullfrog is one of the most important economic aquatic animals in China that is widely cultured in southern China and is a key breed recommended as an industry of poverty alleviation in China. During recent years, a fatal bacterial disease has often been found in cultured bullfrogs. The clinical manifestations of the diseased bullfrogs were severe intestinal inflammation and an anal prolapse. A bacterial pathogen was isolated from the diseased bullfrog intestines. The bacterium was identified as Vibrio cholerae using morphological, biochemical and 16S rRNA phylogenetic analysis. In this study, V. cholerae was isolated and identified in diseased bullfrogs for the first time, providing a basis for the diagnosis and control of the disease. Therefore, attention should be paid to the modes of transmission of V. cholerae from bullfrog and formulate reasonable safety measures.

Consider two-type linear-fractional branching processes in varying environments with asymptotically constant mean matrices. Let $\nu$ be the extinction time. Under certain conditions, we show that both $\mathbb{P}(\nu=n)$ and $\mathbb{P}(\nu>n)$ are asymptotically the same as some functions of the products of spectral radii of the mean matrices. We also give an example for which $\mathbb{P}(\nu=n)$ decays with various speeds such as ${c}/({n^{1/2}\log n)^2}$, ${c}/{n^\beta}$, $\beta >1$, which are very different from those of homogeneous multitype Galton–Watson processes.

In this paper, we study the pricing of vulnerable Asian options with liquidity risk. We employ general Lévy processes to capture the changes in the liquidity discount factors and the information processes of all assets. In the proposed pricing model, we obtain the closed-form pricing formula of vulnerable Asian options using the Fourier transform methods. Finally, the derived pricing formula is used to illustrate the effects of asymmetric jump risk, and the effects are relatively stable on (vulnerable) Asian options with different moneynesses.

The resolution of the Poisson equation is usually one of the most computationally intensive steps for incompressible fluid solvers. Lately, DeepLearning, and especially convolutional neural networks (CNNs), has been introduced to solve this equation, leading to significant inference time reduction at the cost of a lack of guarantee on the accuracy of the solution.This drawback might lead to inaccuracies, potentially unstable simulations and prevent performing fair assessments of the CNN speedup for different network architectures. To circumvent this issue, a hybrid strategy is developed, which couples a CNN with a traditional iterative solver to ensure a user-defined accuracy level. The CNN hybrid method is tested on two flow cases: (a) the flow around a 2D cylinder and (b) the variable-density plumes with and without obstacles (both 2D and 3D), demonstrating remarkable generalization capabilities, ensuring both the accuracy and stability of the simulations. The error distribution of the predictions using several network architectures is further investigated in the plume test case. The introduced hybrid strategy allows a systematic evaluation of the CNN performance at the same accuracy level for various network architectures. In particular, the importance of incorporating multiple scales in the network architecture is demonstrated, since improving both the accuracy and the inference performance compared with feedforward CNN architectures. Thus, in addition to the pure networks’ performance evaluation, this study has also led to numerous guidelines and results on how to build neural networks and computational strategies to predict unsteady flows with both accuracy and stability requirements.

Potentialdata breach losses represent a significant part of operational risk and can be a serious concern for risk managers and insurers. In this paper, we employ the vine copulas under a Bayesian framework to co-model incidences from different data breach types. A full Bayesian approach can allow one to select both the copulas and margins and estimate their parameters in a coherent fashion. In particular, it can incorporate process, parameter, and model uncertainties, and this is very important for applications in risk management under current regulations. We also conduct a series of sensitivity tests on the Bayesian modelling results. Using two public data sets of data breach losses, we find that the overall dependency structure and tail dependence vary significantly between different types of data breaches. The optimally selected vine structure and pairwise copulas suggest more conservative value-at-risk estimates when compared to the other suboptimal copula models.

In the literature, some stochastic orders have been extended to the higher orders in different scenarios. In this paper, inspired by interesting properties of the excess wealth order and its wide range application particularly in comparing the tail variability of risks, we consider the second-order excess wealth order and study its main properties. We obtain two results characterizing the proposed order. We also investigate its relationship with other well-known variability orders and criteria to compare risks. An application of the results in comparing the epoch times of two nonhomogeneous poisson processes is also given.

The study aim was to examine the incidence and risk factors of respiratory syncytial virus (RSV) bronchiolitis hospitalisations and disease severity among infants. We compared demographic and health characteristics of children aged 0–23 hospitalised for RSV bronchiolitis (cases, n = 1227) during 2008–2018 and control children (n = 554) of the same age admitted for non-respiratory disease. RSV antigen was detected in nasal swabs by immunochromatography. Multiple logistic regression models were applied. The average annual incidence of hospitalisation for RSV bronchiolitis was 12.6 per 1000 and 1.7 per 1000 (P < 0.001) among infants and toddlers, respectively, with winter seasonality (November–March). The risk of hospitalisation for RSV bronchiolitis increased among children aged 0–5 months (OR 7.66; 95% CI 5.61–10.45) and 6–11 months (OR 12.88, 95% CI 8.48–19.55), compared to those aged 12–23 months. Additional risk factors were living in low vs. higher socio-economic status towns (OR 1.49; 95% CI 1.14–1.95), having chronic medical conditions (OR 2.75; 95% CI 1.61–4.70), birth month (October–January vs. June–September) (OR 2.19; 95% CI 1.60–2.99) and history of stay in neonatal intensive care unit at birth (OR 2.37; 95% CI 1.27–4.41). Male children and those who had pneumonia were more likely to have severe RSV bronchiolitis. In conclusion, the burden of hospitalisations for RSV bronchiolitis is high, especially in young infants. Effective preventive measures such as RSV active vaccines can reduce the risk of hospitalisations for RSV bronchiolitis among these vulnerable groups.

Data governance is an emerging field of study concerned with how a range of actors can successfully manage data assets according to rules of engagement, decision rights, and accountabilities. Urban studies scholarship has continued to demonstrate and criticize lack of community engagement in smart city development and urban data governance projects, including in local sustainability initiatives. However, few move beyond critique to unpack in more detail what community engagement should look like. To overcome this gap, we develop and test a participatory methodology to identify approaches to empowering community engagement in data governance in the context of the Monash Net Zero Precinct in Melbourne, Australia. Our approach uses design for social innovation to enable a small group of “precinct citizens” to co-design prototypes and multicriteria mapping as a participatory appraisal method to open up and reveal a diversity of perspectives and uncertainties on data governance approaches. The findings reveal the importance of creating deliberative spaces for pluralising community engagement in data governance that consider the diverse values and interests of precinct citizens. This research points toward new ways to conceptualize and design enabling processes of community engagement in data governance and reflects on implementation strategies attuned to the politics of participation to support the embedding of these innovations within specific socio-institutional contexts.

This review aimed to compare the clinical features and CT imaging features between patients with pulmonary tuberculosis (PTB) and lung cancer and patients with PTB alone. That would help to analyse the differences between the two and consequently providing a theoretical basis for the clinical diagnosis and treatment for the patients. Relevant case-control studies focusing on the clinical and CT imaging characteristics between PTB with lung cancer and PTB alone were systematically searched from five electronic databases. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for comparison. As of 2021-07-06, a total of 1735 articles were retrieved. But only 15 articles were finally included for meta-analysis. The results showed a higher proportion of irritable cough, haemorrhagic pleural effusion and lower proportion of night sweating in PTB patients with lung cancer than in PTB patients, and the differences were statistically significant (irritable cough: OR 2.43, 95% CI 1.43–4.11; haemorrhagic pleural effusion: OR 5.73, 95% CI 1.63–20.12; night sweating: OR 0.56, 95% CI 0.36–0.87). In addition, there are many differences in the imaging characteristics of the two types of patients. In conclusion, this review summarises the similarities and differences in clinical symptoms and imaging features between patients with PTB and lung cancer and patients with PTB alone, suggesting that we should be alert to the occurrence of lung cancer in patients with obsolete PTB relapse.

The digital twin concept has developed as a method for extracting value from data, and is being developed as a new technique for the design and asset management of high-value engineering systems such as aircraft, energy generating plant, and wind turbines. In terms of implementation, many proprietary digital twin software solutions have been marketed in this domain. In contrast, this paper describes a recently released open-source software framework for digital twins, which provides a browser-based operational platform using Python and Flask. The new platform is intended to maximize connectivity between users and data obtained from the physical twin. This paper describes how this type of digital twin operational platform (DTOP) can be used to connect the physical twin and other Internet-of-Things devices to both users and cloud computing services. The current release of the software—DTOP-Cristallo—uses the example of a three-storey structure as the engineering asset to be managed. Within DTOP-Cristallo, specific engineering software tools have been developed for use in the digital twin, and these are used to demonstrate the concept. At this stage, the framework presented is a prototype. However, the potential for open-source digital twin software using network connectivity is a very large area for future research and development.

A subset of events within the UK Government Events Research Programme (ERP), developed to examine the risk of transmission of COVID-19 from attendance at events, was examined to explore the public health impact of holding mass sporting events. We used contact tracing data routinely collected through telephone interviews and online questionnaires, to describe the potential public health impact of the large sporting and cultural events on potential transmission and incidence of COVID-19. Data from the EURO 2020 matches hosted at Wembley identified very high numbers of individuals who tested positive for COVID-19 and were traced through NHS Test & Trace. This included both individuals who were potentially infectious (3036) and those who acquired their infection during the time of the Final (6376). This is in contrast with the All England Lawn Tennis Championships at Wimbledon, where there were similar number of spectators and venue capacity but there were lower total numbers of potentially infectious cases (299) and potentially acquired cases (582). While the infections associated with the EURO 2020 event may be attributed to a set of socio-cultural circumstances which are unlikely to be replicated for the forthcoming sporting season, other aspects may be important to consider including mitigations for spectators to consider such as face coverings when travelling to and from events, minimising crowding in poorly ventilated indoor spaces such as bars and pubs where people may congregate to watch events, and reducing the risk of aerosol exposure through requesting that individuals avoid shouting and chanting in large groups in enclosed spaces.

Reaction networks are commonly used within the mathematical biology and mathematical chemistry communities to model the dynamics of interacting species. These models differ from the typical graphs found in random graph theory since their vertices are constructed from elementary building blocks, i.e. the species. We consider these networks in an Erdös–Rényi framework and, under suitable assumptions, derive a threshold function for the network to have a deficiency of zero, which is a property of great interest in the reaction network community. Specifically, if the number of species is denoted by n and the edge probability by $p_n$, then we prove that the probability of a random binary network being deficiency zero converges to 1 if $p_n\ll r(n)$ as $n \to \infty$, and converges to 0 if $p_n \gg r(n)$ as $n \to \infty$, where $r(n)=\frac{1}{n^3}$.

In this paper we analyze a simple spectral method (EIG1) for the problem of matrix alignment, consisting in aligning their leading eigenvectors: given two matrices A and B, we compute two corresponding leading eigenvectors $v_1$ and $v'_{\!\!1}$. The algorithm returns the permutation $\hat{\pi}$ such that the rank of coordinate $\hat{\pi}(i)$ in $v_1$ and that of coordinate i in $v'_{\!\!1}$ (up to the sign of $v'_{\!\!1}$) are the same.

We consider a model of weighted graphs where the adjacency matrix A belongs to the Gaussian orthogonal ensemble of size $N \times N$, and B is a noisy version of A where all nodes have been relabeled according to some planted permutation $\pi$; that is, $B= \Pi^T (A+\sigma H) \Pi $, where $\Pi$ is the permutation matrix associated with $\pi$ and H is an independent copy of A. We show the following zero–one law: with high probability, under the condition $\sigma N^{7/6+\epsilon} \to 0$ for some $\epsilon>0$, EIG1 recovers all but a vanishing part of the underlying permutation $\pi$, whereas if $\sigma N^{7/6-\epsilon} \to \infty$, this method cannot recover more than o(N) correct matches.

This result gives an understanding of the simplest and fastest spectral method for matrix alignment (or complete weighted graph alignment), and involves proof methods and techniques which could be of independent interest.

We give a setting of the Diaconis–Freedman chain in a multi-dimensional simplex and consider its asymptotic behavior. By using techniques from random iterated function theory and quasi-compact operator theory, we first give some sufficient conditions which ensure the existence and uniqueness of an invariant probability measure and, in particular cases, explicit formulas for the invariant probability density. Moreover, we completely classify all behaviors of this chain in dimension two. Some other settings of the chain are also discussed.