Generalised Probabilistic Control Design for Uncertain Stochastic Control Systems

In this paper a novel generalised fully probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented for a linear Gaussian uncertain class of stochastic systems. A single layer neural network is used to approximate the probability density function of the system dynamics. The generalised probabilistic control law is obtained by solving the recurrence equation of dynamic programming to the fully probabilistic design control problem while taking into consideration the dependency of the parameters of the estimated probability density function of the system dynamics on the input values. It is shown to be of the class of cautious type controllers which accurately minimises the value of the Kullback-Leibler divergence without disregarding the variance of the model prediction as an element to be minimised. Comparison of theoretical and numerical results obtained from the F-16 fighter aircraft application with existing state-of-the-art demonstrates the effectiveness of the proposed method

Dealing with a traumatic past: the victim hearings of the South African truth and reconciliation commission and their reconciliation discourse

In the final years of the twentieth and the beginning of the twenty-first century, there has been a worldwide tendency to approach conflict resolution from a restorative rather than from a retributive perspective. The South African Truth and Reconciliation Commission (TRC), with its principle of 'amnesty for truth' was a turning point. Based on my discursive research of the TRC victim hearings, I would argue that it was on a discursive level in particular that the Truth Commission has exerted/is still exerting a long-lasting impact on South African society. In this article, three of these features will be highlighted and illustrated: firstly, the TRC provided a discursive forum for thousands of ordinary citizens. Secondly, by means of testimonies from apartheid victims and perpetrators, the TRC composed an officially recognised archive of the apartheid past. Thirdly, the reconciliation discourse created at the TRC victim hearings formed a template for talking about a traumatic past, and it opened up the debate on reconciliation. By discussing these three features and their social impact, it will become clear that the way in which the apartheid past was remembered at the victim hearings seemed to have been determined, not so much by political concerns, but mainly by social needs

Repeatability and Reliability of New Air and Water Permeability Tests for Assessing the Durability of High Performance Concretes

This paper reports on the accuracy of new test methods developed to measure the air and water permeability of high-performance concretes (HPCs). Five representative HPC and one normal concrete (NC) mixtures were tested to estimate both repeatability and reliability of the proposed methods. Repeatability acceptance was adjudged using values of signal-noise ratio (SNR) and discrimination ratio (DR), and reliability was investigated by comparing against standard laboratory-based test methods (i.e., the RILEM gas permeability test and BS EN water penetration test). With SNR and DR values satisfying recommended criteria, it was concluded that test repeatability error has no significant influence on results. In addition, the research confirmed strong positive relationships between the proposed test methods and existing standard permeability assessment techniques. Based on these findings, the proposed test methods show strong potential to become recognized as international methods for determining the permeability of HPCs

Efficacy of individualised starting dose (isd) and fixed starting dose (fsd) of niraparib per investigator assessment (ia) in newly diagnosed advanced ovarian cancer (oc) patients

Niraparib is a poly(ADP-ribose) polymerase inhibitor approved for maintenance treatment of patients with newly diagnosed or recurrent OC that responded to platinumbased chemotherapy and treatment in heavily-pretreated recurrent OC. Here we report efficacy in patients receiving the FSD and ISD in the PRIMA/ENGOT-OV26/GOG-3012 trial (NCT02655016)

Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study

BACKGROUND: Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen density in a variety of clinical and epidemiological contexts. These methods are often classified as semi-quantitative, yet estimates of reliability or sensitivity are seldom reported. Here, a statistical framework is developed for assessing the reliability (uncertainty) of pathogen densities estimated using QMMs and the associated diagnostic sensitivity. The method is illustrated with quantification of Plasmodium falciparum gametocytaemia by QT-NASBA. RESULTS: The reliability of pathogen (e.g. gametocyte) densities, and the accompanying diagnostic sensitivity, estimated by two contrasting statistical calibration techniques, are compared; a traditional method and a mixed model Bayesian approach. The latter accounts for statistical dependence of QMM assays run under identical laboratory protocols and permits structural modelling of experimental measurements, allowing precision to vary with pathogen density. Traditional calibration cannot account for inter-assay variability arising from imperfect QMMs and generates estimates of pathogen density that have poor reliability, are variable among assays and inaccurately reflect diagnostic sensitivity. The Bayesian mixed model approach assimilates information from replica QMM assays, improving reliability and inter-assay homogeneity, providing an accurate appraisal of quantitative and diagnostic performance. CONCLUSIONS: Bayesian mixed model statistical calibration supersedes traditional techniques in the context of QMM-derived estimates of pathogen density, offering the potential to improve substantially the depth and quality of clinical and epidemiological inference for a wide variety of pathogens

The impact of measurement errors in the identification of regulatory networks

<p>Abstract</p> <p>Background</p> <p>There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise.</p> <p>Results</p> <p>This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data.</p> <p>Conclusions</p> <p>Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.</p