Asymptotic optimality of RESTART estimators in highly dependable systems

Abstract We consider a wide class of models that includes the highly reliable Markovian systems (HRMS) often used to represent the evolution of multi-component systems in reliability settings. Repair times and component lifetimes are random variables that follow a general distribution, and the repair service adopts a priority repair rule based on system failure risk. Since crude simulation has proved to be inefficient for highly-dependable systems, the RESTART method is used for the estimation of steady-state unavailability and other reliability measures. In this method, a number of simulation retrials are performed when the process enters regions of the state space where the chance of occurrence of a rare event (e.g., a system failure) is higher. The main difficulty involved in applying this method is finding a suitable function, called the importance function, to define the regions. In this paper we introduce an importance function which, for unbalanced systems, represents a great improvement over the importance function used in previous papers. We also demonstrate the asymptotic optimality of RESTART estimators in these models. Several examples are presented to show the effectiveness of the new approach, and probabilities up to the order of 10-42 are accurately estimated with little computational effort.

How the brain keeps the eyes still

The brain can hold the eyes still because it stores a memory of eye position. The brain’s memory of horizontal eye position appears to be represented by persistent neural activity in a network known as the neural integrator, which is localized in the brainstem and cerebellum. Existing experimental data are reinterpreted as evidence for an “attractor hypothesis” that the persistent patterns of activity observed in this network form an attractive line of fixed points in its state space. Line attractor dynamics can be produced in linear or nonlinear neural networks by learning mechanisms that precisely tune positive feedback.

Leishmaniose visceral americana: avaliação dos parâmetros da capacidade vetorial de Lutzomyia longipalpis em área urbana do muncípio de Panorama, São Paulo, Brasil

Introdução: A leishmaniose visceral (LV) é um importante problema de saúde pública no Brasil, com cerca 3000 mil casos notificados anualmente. Nos últimos anos, a LV tem ampliado sua distribuição em vários estados do país, associada principalmente aos processos socioambientais, antrópicos e migratórios. A LV é causada pela infecção com Leishmania infantum chagasi, transmitida, principalmente, por Lutzomyia longipalpis (Diptera: Psychodidae). Este flebotomíneo apresenta ampla distribuição nas Américas, todavia, evidências sugerem que se constitui em um complexo de espécies crípticas. A dinâmica de transmissão da LV é modulada por fatores ecológicos locais que influenciam a interação entre populações do patógeno, do vetor e dos hospedeiros vertebrados. Portanto, o estudo das variáveis associadas a esta interação pode contribuir para elucidar aspectos dos elos epidemiológicos e contribuir para a tomada de decisões em saúde pública. Objetivo: Avaliar parâmetros relacionados à capacidade vetorial da população de Lu. longipalpis presente em área urbana do município de Panorama, estado de São Paulo. Métodos: Foram realizadas capturas mensais durante 48 meses para avaliar a distribuição espaço-temporal de Lu. longipalpis e investigar a circulação de Le. i. chagasi. Também foram realizados os seguintes experimentos com o vetor: captura-marcação-soltura-recaptura para estimar a sobrevida da população e a duração do seu ciclo gonotrófico, a atratividade dos hospedeiros mais frequentes em áreas urbanas, a proporção de repasto em cão, infecção experimental e competência vetorial. Resultados: Observou-se que no município de Panorama, Lu. longipalpis apresentou as frequências mais elevadas na estação chuvosa (entre outubro e março), maior densidade em áreas com presença de vegetação e criação de animais domésticos, locais aonde também foi demonstrada a circulação natural de espécimes de Lu. longipalpis infectados com Le. i. chagasi. Além disto, foi corroborado que a população de Lu. longipalpis apresentou hábito hematofágico eclético, altas taxas de sobrevivência e que foi competente para transmitir o agente da LV. Nos experimentos de laboratório foi evidenciada a heterogeneidade na infecção de fêmeas de Lu. longipalpis desafiadas a se alimentarem em cães comprovadamente infectados por L. i. chagasi e o rápido desenvolvimento do parasita neste vetor natural. Conclusões. As observações do presente estudo corroboram a capacidade vetora de Lu. longipalpis para transmitir a Le. i. chagasi e ressaltam a importância da espécie na transmissão do agente etiológico da LV. Ações de manejo ambiental, educação e promoção à saúde são recomendadas às autoridades municipais para diminuir o risco potencial de infecção na população humana e canina, considerando-se o elevado potencial vetor de Lu. longipalpis e a presença de condições que favorecem a interação dos componentes da tríade epidemiológica da LV.

Aplicação e avaliação de desempenho de um sistema de otimização em tempo real em uma unidade de produção de propeno.

Com o objetivo de aumentar o lucro de plantas químicas, a Otimização em Tempo Real (RTO) é uma ferramenta que busca determinar as condições ótimas operacionais do processo em estado estacionário, respeitando as restrições operacionais estabelecidas. Neste trabalho foi realizada a implementação prática de um ciclo RTO em um processo de destilação por recompressão de vapor (VRD), propileno-propano, da Refinaria de Paulínia (Petrobras S.A.), a partir de dados históricos da planta. Foram consideradas as principais etapas de um ciclo clássico de RTO: identificação de estado estacionário, reconciliação de dados, estimação de parâmetros e otimização econômica. Essa unidade foi modelada, simulada e otimizada em EMSO (Environment for Modeling, Simulation and Optimization), um simulador de processos orientado a equações desenvolvido no Brasil. Foram analisados e comparados dois métodos de identificação de estado estacionário, um baseado no teste estatístico F e outro baseado em wavelets. Ambos os métodos tiveram resultados semelhantes e mostraram-se capazes de identificar os estados estacionários de forma satisfatória, embora seja necessário o ajuste de parâmetros na sua implementação. Foram identificados alguns pontos estacionários para serem submetidos ao ciclo RTO e foi possível verificar a importância de partir de um estado estacionário para a continuidade do ciclo, já que essa é uma premissa do método. A partir dos pontos analisados, os resultados deste estudo mostram que o RTO é capaz de aumentar o ganho econômico entre 2,5-24%, dependendo das condições iniciais consideradas, o que pode representar ganhos de até 18 milhões de dólares por ano. Além disso, para essa unidade, verificou-se que o compressor é um equipamento limitante no aumento de ganho econômico do processo.

Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy

In this letter, a new approach for crop phenology estimation with remote sensing is presented. The proposed methodology is aimed to exploit tools from a dynamical system context. From a temporal sequence of images, a geometrical model is derived, which allows us to translate this temporal domain into the estimation problem. The evolution model in state space is obtained through dimensional reduction by a principal component analysis, defining the state variables, of the observations. Then, estimation is achieved by combining the generated model with actual samples in an optimal way using a Kalman filter. As a proof of concept, an example with results obtained with this approach over rice fields by exploiting stacks of TerraSAR-X dual polarization images is shown.

Dynamical Approach for Real-Time Monitoring of Agricultural Crops

In this paper, a novel approach for exploiting multitemporal remote sensing data focused on real-time monitoring of agricultural crops is presented. The methodology is defined in a dynamical system context using state-space techniques, which enables the possibility of merging past temporal information with an update for each new acquisition. The dynamic system context allows us to exploit classical tools in this domain to perform the estimation of relevant variables. A general methodology is proposed, and a particular instance is defined in this study based on polarimetric radar data to track the phenological stages of a set of crops. A model generation from empirical data through principal component analysis is presented, and an extended Kalman filter is adapted to perform phenological stage estimation. Results employing quad-pol Radarsat-2 data over three different cereals are analyzed. The potential of this methodology to retrieve vegetation variables in real time is shown.

Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and Temperature

In this study, a methodology based in a dynamical framework is proposed to incorporate additional sources of information to normalized difference vegetation index (NDVI) time series of agricultural observations for a phenological state estimation application. The proposed implementation is based on the particle filter (PF) scheme that is able to integrate multiple sources of data. Moreover, the dynamics-led design is able to conduct real-time (online) estimations, i.e., without requiring to wait until the end of the campaign. The evaluation of the algorithm is performed by estimating the phenological states over a set of rice fields in Seville (SW, Spain). A Landsat-5/7 NDVI series of images is complemented with two distinct sources of information: SAR images from the TerraSAR-X satellite and air temperature information from a ground-based station. An improvement in the overall estimation accuracy is obtained, especially when the time series of NDVI data is incomplete. Evaluations on the sensitivity to different development intervals and on the mitigation of discontinuities of the time series are also addressed in this work, demonstrating the benefits of this data fusion approach based on the dynamic systems.

The North Sea autumn spawning Herring (Clupea harengus L.) Spawning Component Abundance Index (SCAI)

The North Sea autumn-spawning herring (Clupea harengus) stock consists of a set of different spawning components. The dynamics of the entire stock have been well characterized, but although time-series of larval abundance indices are available for the individual components, study of the dynamics at the component level has historically been hampered by missing observations and high sampling noise. A simple state-space statistical model is developed that is robust to these problems, gives a good fit to the data, and proves capable of both handling and predicting missing observations well. Furthermore, the sum of the fitted abundance indices across all components proves an excellent proxy for the biomass of the total stock, even though the model utilizes information at the individual-component level. The Orkney-Shetland component appears to have recovered faster from historic depletion events than the other components, whereas the Downs component has been the slowest. These differences give rise to changes in stock composition, which are shown to vary widely within a relatively short time. The modelling framework provides a valuable tool for studying and monitoring the dynamics of the individual components of the North Sea herring stock.

Strategic plan.

Mode of access: Internet.

Existence and uniqueness of Q-processes with a given finite μ-invariant measure

Let Q be a stable and conservative Q-matrix over a countable state space S consisting of an irreducible class C and a single absorbing state 0 that is accessible from C. Suppose that Q admits a finite mu-subinvariant measure in on C. We derive necessary and sufficient conditions for there to exist a Q-process for which m is mu-invariant on C, as well as a necessary condition for the uniqueness of such a process.

Impact of an intensive nursing education course on nurses' knowledge, confidence, attitudes, and perceived skills in the care of patients with cancer

Purpose/Objectives: To evaluate the impact of a cancer nursing education course on RNs. Design: Quasi-experimental, longitudinal, pretest/post-test design, with a follow-up assessment six weeks after the completion of the nursing education course. Setting: Urban, nongovernment, cancer control agency in Australia. Sample: 53 RNs, of whom 93% were female, with a mean age of 44.6 years and a mean of 16.8 years of experience in nursing; 86% of the nurses resided and worked in regional areas outside of the state capital. Methods: Scales included the Intervention With Psychosocial Needs: Perceived Importance and Skill Level Scale, Palliative Care Quiz for Nurses, Breast Cancer Knowledge, Preparedness for Cancer Nursing, and Satisfaction With Learning. Data were analyzed using multiple analysis of variance and paired t tests. Main Research Variables: Cancer nursing-related knowledge, preparedness for cancer nursing, and attitudes toward and perceived skills in the psychosocial care of patients with cancer and their families. Findings: Compared to nurses in the control group, nurses who attended the nursing education course improved in their cancer nursing-related knowledge, preparedness for cancer nursing, and attitudes toward and perceived skills in the psychosocial care of patients with cancer and their families. Improvements were evident at course completion and were maintained at the six-week follow-up assessment. Conclusions: The nursing education course was effective in improving nurses' scores on all outcome variables. Implications for Nursing: Continuing nursing education courses that use intensive mode timetabling, small group learning, and a mix of teaching methods, including didactic and interactive approaches and clinical placements, are effective and have the potential to improve nursing practice in oncology.

Subgeometric rates of convergence for a class of continuous-time Markov process

Let (Phi(t))(t is an element of R+) be a Harris ergodic continuous-time Markov process on a general state space, with invariant probability measure pi. We investigate the rates of convergence of the transition function P-t(x, (.)) to pi; specifically, we find conditions under which r(t) vertical bar vertical bar P-t (x, (.)) - pi vertical bar vertical bar -> 0 as t -> infinity, for suitable subgeometric rate functions r(t), where vertical bar vertical bar - vertical bar vertical bar denotes the usual total variation norm for a signed measure. We derive sufficient conditions for the convergence to hold, in terms of the existence of suitable points on which the first hitting time moments are bounded. In particular, for stochastically ordered Markov processes, explicit bounds on subgeometric rates of convergence are obtained. These results are illustrated in several examples.

Output price subsidies in a stochastic world

This article studies the comparative statics of output subsidies for firms, with monotonic preferences over costs and returns, that face price and production uncertainty. The modeling of deficiency payments, support-price schemes, and stochastic supply shifts in a state-space framework is discussed. It is shown how these notions can be used, via a simple application of Shephard's lemma, to analyze input-demand shifts once comparative-static results for supply are available. A range of comparative-static results for supply are then developed and discussed.

First-principles quantum dynamics in interacting Bose gases II: stochastic gauges

First principles simulations of the quantum dynamics of interacting Bose gases using the stochastic gauge representation are analysed. In a companion paper, we showed how the positive-P representation can be applied to these problems using stochastic differential equations. That method, however, is limited by increased sampling error as time evolves. Here, we show how the sampling error can be greatly reduced and the simulation time significantly extended using stochastic gauges. In particular, local stochastic gauges (a subset) are investigated. Improvements are confirmed in numerical calculations of single-, double- and multi-mode systems in the weak-mode coupling regime. Convergence issues are investigated, including the recognition of two modes by which stochastic equations produced by phase-space methods in general can diverge: movable singularities and a noise-weight relationship. The example calculated here displays wave-like behaviour in spatial correlation functions propagating in a uniform 1D gas after a sudden change in the coupling constant. This could in principle be tested experimentally using Feshbach resonance methods.

Gaussian operator bases for correlated fermions

We formulate a general multi-mode Gaussian operator basis for fermions, to enable a positive phase-space representation of correlated Fermi states. The Gaussian basis extends existing bosonic phase-space methods to Fermi systems and thus allows first-principles dynamical or equilibrium calculations in quantum many-body Fermi systems. We prove the completeness of the basis and derive differential forms for products with one- and two-body operators. Because the basis satisfies fermionic superselection rules, the resulting phase space involves only c-numbers, without requiring anticommuting Grassmann variables. Furthermore, because of the overcompleteness of the basis, the phase-space distribution can always be chosen positive. This has important consequences for the sign problem in fermion physics.