Hyperglycaemia is prevalent in critical care, and tight control can reduce mortality by 29 -45%. Targeted glucose control can be achieved by frequent fitting and prediction of a modelled insulin sensitivity index, S I . This parameter varies significantly in the critically ill due to condition evolution and drug therapies. A 3-D stochastic model of hourly S I variability is constructed using retrospective data from 18 long term critical care patients. The model can be used to produce the blood glucose level probability distribution one hour following a known insulin and/or nutrition intervention. Thus, it enables accurate prediction for glycemic control based on confidence intervals.

In order to incorporate seismic risk of facilities into a decision making framework, procedures are needed to quantify such risk for stakeholders. Seismic loss estimation methods combine seismic hazard, structural response, damage fragility, and damage consequences to allow quantification of seismic risk. This paper presents a loss estimation methodology which allows various means of quantifying seismic risk of a specific facility. The methodology is component-based and can therefore distinguish between different structural configurations or different facility contents and is consistent with state-of-the-art loss assessment procedures. Loss is measured in the forms of direct structural and non-structural repair costs, and although not considered in the example, business disruption and occupant casualties can also be considered. This framework has been packaged in a computer code available for future dissemination in the public domain so that users need only to have a basic understanding of the methodology and the input data that is required. Discussion is given to the flexibility of the framework in terms of the rigour which can be employed at each of the main steps in the procedure. Via a case study of a high-rise office building, the use of the methodology in decision-making is illustrated. Methodological requirements and further research directions are discussed.

The model-based insulin sensitivity parameter, SI, highly correlates to ISIG in all subgroups, even when only considering a transient state. The high correlation of SI offers the potential for a short, simple yet highly correlated, model-based assessment of insulin sensitivity that is not currently available.

In this paper the efficacy of an approximate method of uncertainty propagation, known as the first-order second-moment (FOSM) method, for use in seismic loss estimation is investigated. The governing probabilistic equations which define the Pacific Earthquake Engineering Research (PEER)-based loss estimation methodology used are discussed, and the proposed locations to use the FOSM approximations identified. The justification for the use of these approximations is based on a significant reduction in computational time by not requiring direct numerical integration, and the fact that only the first two moments of the distribution are known. Via various examples it is shown that great care should be taken in the use of such approximations, particularly considering the large uncertainties that must be propagated in a seismic loss assessment. Finally, a complete loss assessment of a structure is considered to investigate in detail the location where significant approximation errors are incurred, where caution must be taken in the interpretation of the results, and the computational demand of the various alternatives. KEYWORDSPerformance-based earthquake engineering (PBEE); aleatory uncertainty; epistemic uncertainty; first-order second-moment (FOSM) method; loss estimation; loss deaggregation.2

Inference for the stochastic blockmodel is currently of burgeoning interest in the statistical community, as well as in various application domains as diverse as social networks, citation networks, brain connectivity networks (connectomics), etc. Recent theoretical developments have shown that spectral embedding of graphs yields tractable distributional results; in particular, a random dot product latent position graph formulation of the stochastic blockmodel informs a mixture of normal distributions for the adjacency spectral embedding. We employ this new theory to provide an empirical Bayes methodology for estimation of block memberships of vertices in a random graph drawn from the stochastic blockmodel, and demonstrate its practical utility. The posterior inference is conducted using a Metropolis-within-Gibbs algorithm. The theory and methods are illustrated through Monte Carlo simulation studies, both within the stochastic blockmodel and beyond, and experimental results on a Wikipedia graph are presented.