Brief tools are necessary to identify adolescents at greatest risk for ART non-adherence. From the WHO’s HEADSS/HEADSS+ adolescent wellbeing checklists, we identify constructs strongly associated with non-adherence (validated with viral load). We conducted interviews and collected clinical records from a 3-year cohort of 1046 adolescents living with HIV from 52 South African government facilities. We used least absolute shrinkage and selection operator variable selection approach with a generalized linear mixed model. HEADSS constructs most predictive were: violence exposure (aOR 1.97, CI 1.61; 2.42, p < 0.001), depression (aOR 1.71, CI 1.42; 2.07, p < 0.001) and being sexually active (aOR 1.80, CI 1.41; 2.28, p < 0.001). Risk of non-adherence rose from 20.4% with none, to 55.6% with all three. HEADSS+ constructs were: medication side effects (aOR 2.27, CI 1.82; 2.81, p < 0.001), low social support (aOR 1.97, CI 1.60; 2.43, p < 0.001) and non-disclosure to parents (aOR 2.53, CI 1.91; 3.53, p < 0.001). Risk of non-adherence rose from 21.6% with none, to 71.8% with all three. Screening within established checklists can improve identification of adolescents needing increased support. Adolescent HIV services need to include side-effect management, violence prevention, mental health and sexual and reproductive health.

Abstract We apply deep kernel learning (DKL), which can be viewed as a combination of a Gaussian process (GP) and a deep neural network (DNN), to compression ignition engine emissions and compare its performance to a selection of other surrogate models on the same dataset. Surrogate models are a class of computationally cheaper alternatives to physics-based models. High-dimensional model representation (HDMR) is also briefly discussed and acts as a benchmark model for comparison. We apply the considered methods to a dataset, which was obtained from a compression ignition engine and includes as outputs soot and NOx emissions as functions of 14 engine operating condition variables. We combine a quasi-random global search with a conventional grid-optimization method in order to identify suitable values for several DKL hyperparameters, which include network architecture, kernel, and learning parameters. The performance of DKL, HDMR, plain GPs, and plain DNNs is compared in terms of the root mean squared error (RMSE) of the predictions as well as computational expense of training and evaluation. It is shown that DKL performs best in terms of RMSE in the predictions whilst maintaining the computational cost at a reasonable level, and DKL predictions are in good agreement with the experimental emissions data.

This paper addresses the issue of modeling meanfield behavior in heterogeneous populations of linear timeinvariant SISO systems. Our analysis is conducted in the frequency domain, where the heterogeneity of input-output mappings (transfer functions) is modeled as a complex-valued Gaussian process. The mean-field model of diffusively coupled agents is obtained as a Gaussian approximation of averaged input-output behavior. It is shown that the strong coupling and the large number of agents reduce the population variance.

The spatially distributed reaction networks are indispensable for the understanding of many important phenomena concerning the development of organisms, coordinated cell behavior, and pattern formation. The purpose of this brief discussion paper is to point out some open problems in the theory of PDE and compartmental ODE models of balanced reaction-diffusion networks.

Stokes-Dirac structures are infinite-dimensional Dirac structures defined in terms of differential forms on a smooth manifold with boundary. These Dirac structures lay down a geometric framework for the formulation of Hamiltonian systems with a nonzero boundary energy flow. Simplicial triangulation of the underlaying manifold leads to the so-called simplicial Dirac structures, discrete analogues of Stokes-Dirac structures, and thus provides a natural framework for deriving finite-dimensional port-Hamiltonian systems that emulate their infinite-dimensional counterparts. The port-Hamiltonian systems defined with respect to Stokes-Dirac and simplicial Dirac structures exhibit gauge and a discrete gauge symmetry, respectively. In this paper, employing Poisson reduction we offer a unified technique for the symmetry reduction of a generalized canonical infinite-dimensional Dirac structure to the Poisson structure associated with Stokes-Dirac structures and of a fine-dimensional Dirac structure to simplicial Dirac structures. We demonstrate this Poisson scheme on a physical example of the vibrating string.