Space–time zero-inflated count models of Harbor seals

Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space–time random effects in zero-inflated Poisson (ZIP) and ‘hurdle’ models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice grid of samples, of harbor seals hauled out on glacial ice in Disenchantment Bay, near Yakutat, Alaska. A hurdle model is similar to a ZIP model except it does not mix zeros from the binary and count processes. Both models can be used for zero-inflated data, and we compared space–time ZIP and hurdle models in a Bayesian hierarchical model. Space–time ZIP and hurdle models were constructed by using spatial conditional autoregressive (CAR) models and temporal first-order autoregressive (AR(1)) models as random effects in ZIP and hurdle regression models. We created maps of smoothed predictions for harbor seal counts based on ice density, other covariates, and spatio-temporal random effects. For both models predictions around the edges appeared to be positively biased. The linex loss function is an asymmetric loss function that penalizes overprediction more than underprediction, and we used it to correct for prediction bias to get the best map for space–time ZIP and hurdle models.

Self-exciting threshold binomial autoregressive processes

We introduce a new class of integer-valued self-exciting threshold models, which is based on the binomial autoregressive model of order one as introduced by McKenzie (Water Resour Bull 21:645–650, 1985. doi:10.1111/j.1752-1688.1985. tb05379.x). Basic probabilistic and statistical properties of this class of models are discussed. Moreover, parameter estimation and forecasting are addressed. Finally, the performance of these models is illustrated through a simulation study and an empirical application to a set of measle cases in Germany.

Contributos para a análise estatística de séries de contagem

A análise das séries temporais de valores inteiros tornou-se, nos últimos anos, uma área de investigação importante, não só devido à sua aplicação a dados de contagem provenientes de diversos campos da ciência, mas também pelo facto de ser uma área pouco explorada, em contraste com a análise séries temporais de valores contínuos. Uma classe que tem obtido especial relevo é a dos modelos baseados no operador binomial thinning, da qual se destaca o modelo auto-regressivo de valores inteiros de ordem p. Esta classe é muito vasta, pelo que este trabalho tem como objectivo dar um contributo para a análise estatística de processos de contagem que lhe pertencem. Esta análise é realizada do ponto de vista da predição de acontecimentos, aos quais estão associados mecanismos de alarme, e também da introdução de novos modelos que se baseiam no referido operador. Em muitos fenómenos descritos por processos estocásticos a implementação de um sistema de alarmes pode ser fundamental para prever a ocorrência de um acontecimento futuro. Neste trabalho abordam-se, nas perspectivas clássica e bayesiana, os sistemas de alarme óptimos para processos de contagem, cujos parâmetros dependem de covariáveis de interesse e que variam no tempo, mais concretamente para o modelo auto-regressivo de valores inteiros não negativos com coeficientes estocásticos, DSINAR(1). A introdução de novos modelos que pertencem à classe dos modelos baseados no operador binomial thinning é feita quando se propõem os modelos PINAR(1)T e o modelo SETINAR(2;1). O modelo PINAR(1)T tem estrutura periódica, cujas inovações são uma sucessão periódica de variáveis aleatórias independentes com distribuição de Poisson, o qual foi estudado com detalhe ao nível das suas propriedades probabilísticas, métodos de estimação e previsão. O modelo SETINAR(2;1) é um processo auto-regressivo de valores inteiros, definido por limiares auto-induzidos e cujas inovações formam uma sucessão de variáveis independentes e identicamente distribuídas com distribuição de Poisson. Para este modelo estudam-se as suas propriedades probabilísticas e métodos para estimar os seus parâmetros. Para cada modelo introduzido, foram realizados estudos de simulação para comparar os métodos de estimação que foram usados.

An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata

Modelling an environmental process involves creating a model structure and parameterising the model with appropriate values to accurately represent the process. Determining accurate parameter values for environmental systems can be challenging. Existing methods for parameter estimation typically make assumptions regarding the form of the Likelihood, and will often ignore any uncertainty around estimated values. This can be problematic, however, particularly in complex problems where Likelihoods may be intractable. In this paper we demonstrate an Approximate Bayesian Computational method for the estimation of parameters of a stochastic CA. We use as an example a CA constructed to simulate a range expansion such as might occur after a biological invasion, making parameter estimates using only count data such as could be gathered from field observations. We demonstrate ABC is a highly useful method for parameter estimation, with accurate estimates of parameters that are important for the management of invasive species such as the intrinsic rate of increase and the point in a landscape where a species has invaded. We also show that the method is capable of estimating the probability of long distance dispersal, a characteristic of biological invasions that is very influential in determining spread rates but has until now proved difficult to estimate accurately.

Measurements of particle emissions from nanotechnology processes, with assessment of measuring techniques and workplace controls

The overall aim of this project was to contribute to existing knowledge regarding methods for measuring characteristics of airborne nanoparticles and controlling occupational exposure to airborne nanoparticles, and to gather data on nanoparticle emission and transport in various workplaces. The scope of this study involved investigating the characteristics and behaviour of particles arising from the operation of six nanotechnology processes, subdivided into nine processes for measurement purposes. It did not include the toxicological evaluation of the aerosol and therefore, no direct conclusion was made regarding the health effects of exposure to these particles. Our research included real-time measurement of sub, and supermicrometre particle number and mass concentration, count median diameter, and alveolar deposited surface area using condensation particle counters, an optical particle counter, DustTrak photometer, scanning mobility particle sizer, and nanoparticle surface area monitor, respectively. Off-line particle analysis included scanning and transmission electron microscopy, energy-dispersive x-ray spectrometry, and thermal optical analysis of elemental carbon. Sources of fibrous and non-fibrous particles were included.

Alive SMC^2: Bayesian model selection for low-count time series models with intractable likelihoods

In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.

Do motor vehicle crashes arise from single or multiple unique risk processes? An inquiry into crash causes and modelling

Crashes at any particular transport network location consist of a chain of events arising from a multitude of potential causes and/or contributing factors whose nature is likely to reflect geometric characteristics of the road, spatial effects of the surrounding environment, and human behavioural factors. It is postulated that these potential contributing factors do not arise from the same underlying risk process, and thus should be explicitly modelled and understood. The state of the practice in road safety network management applies a safety performance function that represents a single risk process to explain crash variability across network sites. This study aims to elucidate the importance of differentiating among various underlying risk processes contributing to the observed crash count at any particular network location. To demonstrate the principle of this theoretical and corresponding methodological approach, the study explores engineering (e.g. segment length, speed limit) and unobserved spatial factors (e.g. climatic factors, presence of schools) as two explicit sources of crash contributing factors. A Bayesian Latent Class (BLC) analysis is used to explore these two sources and to incorporate prior information about their contribution to crash occurrence. The methodology is applied to the state controlled roads in Queensland, Australia and the results are compared with the traditional Negative Binomial (NB) model. A comparison of goodness of fit measures indicates that the model with a double risk process outperforms the single risk process NB model, and thus indicating the need for further research to capture all the three crash generation processes into the SPFs.

Using Mastitis Records and Somatic Cell Count Data

On-farm records are essential for managing mastitis in dairy herds. Mastitis records are a useful tool for caring for an individual cow, to monitor compliance of farm personnel working with groups of animals, to understand the epidemiology of mastitis in the herd, to ensure responsible drug utilization, and to document accountability in care of the cow. Herds have become larger and more people are involved with individual animal care. This article describes a records plan that can be used to monitor mastitis at the herd level, aid in decision-making processes for individual cows, and improve drug use on dairy herds.

Qualitatitive and quantitative analysis of fish spoilage processes for development of new food monitoring systems

This study investigates the growth and metabolite production of microorganisms causing spoilage of Atlantic cod (Gadus morhua) fillets packaged under air and modified atmosphere (60 % CO2, 40 % O2). Samples were provided by two different retailers (A and B). Storage of packaged fillets occurred at 4 °C and 8 °C. Microbiological quality and metabolite production of cod fillets stored in MAP 4 °C, MAP 8 °C and air were monitored during 13 days, 7 days and 3 days of storage, respectively. Volatile compounds concentration in the headspace were quantified by Selective ion flow tube mass spectrometry and a correlation with microbiological spoilage was studied. The onset of volatile compounds detection was observed to be mostly around 7 log cfu/g of total psychrotrophic count. Trimethylamine and dimethyl sulfide were found to be the dominant volatiles in all of the tested storage conditions, nevertheless there was no close correlation between concentrations of each main VOC and percentages of rejection based on sensory evaluation. According to results it was concluded that they cannot be considered as only indicators of the quality of cod fillets stored in modified atmosphere and air.  

Analyzing Panel Count Data with Informative Observation Times

In this paper, we study panel count data with informative observation times. We assume nonparametric and semiparametric proportional rate models for the underlying recurrent event process, where the form of the baseline rate function is left unspecified and a subject-specific frailty variable inflates or deflates the rate function multiplicatively. The proposed models allow the recurrent event processes and observation times to be correlated through their connections with the unobserved frailty; moreover, the distributions of both the frailty variable and observation times are considered as nuisance parameters. The baseline rate function and the regression parameters are estimated by maximizing a conditional likelihood function of observed event counts and solving estimation equations. Large sample properties of the proposed estimators are studied. Numerical studies demonstrate that the proposed estimation procedures perform well for moderate sample sizes. An application to a bladder tumor study is presented to illustrate the use of the proposed methods.

Independent association of sleep quality, fatigue, and vital exhaustion with platelet count in patients with a previous venous thromboembolic event

Elevated platelet count might reflect increased inflammation as an etiological factor for venous thromboembolism (VTE). Poor sleep, fatigue, and exhaustion are all associated with inflammation and are also common sequelae of chronic psychological stress that previously predicted increased risk of VTE. We hypothesized that platelet count would be high in patients with VTE who sleep poorly and who are fatigued and exhausted. We investigated 205 patients scheduled for thrombophilia work-up > or =3 months after an objectively diagnosed venous thromboembolic event. They completed the Jenkins Sleep Questionnaire to rate subjective sleep quality and the short forms of the Multidimensional Fatigue Symptom Inventory and Maastricht Vital Exhaustion Questionnaire. Platelet count was determined by a mechanical Coulter counter. Analyses controlled for age, sex, body mass index, time since the index event, and medication. After taking into account these covariates, poorer sleep quality (p = 0.001; DeltaR(2)= 0.046), high fatigue (p = 0.008; DeltaR(2)= 0.032), and vital exhaustion (p = 0.050; DeltaR(2)= 0.017) were all associated with elevated platelet count. In addition, high level of fatigue mediated the relationship between poor sleep quality and elevated platelet count (p = 0.046). Poor sleep quality, high levels of fatigue, and vital exhaustion were identified as correlates of an elevated platelet count in patients with a previous episode of VTE. Given the emerging role of inflammatory processes in VTE, the findings suggest a mechanism through which behavioral and chronic psychological stressors might contribute to incident and recurrent venous thrombotic events.

The Chemical Mechanism of a Brown-Rot Decay Mimtic System and its Application in Paper Recycling Processes

This work is aimed at improving our current knowledge of the non-enzymatic inecl~anisins involved in brown-rot decay, as well as the exploration of potential applications of a brown-rot mimetic model system in paper recycling processes. The study was divided into two parts. The first part focussed on the chemical mechanisms involved in chelation and reduction of iron by a low molecular weight chelator (isolated from the brown-rot fungus Gloeophyllz~m tmbeum) and its model compound 2,3- dihydroxybenzoic acid (2,3-DHBA). Chelation as well as free radical generation mediated by this system were studied by ESR measurement. The results indicate that the effects of the chelator/iron ratio, the pH, and other reaction parameters on hydroxyl radical generation by a Fenton type system could be determined using ESR spin-trapping techniques. The results also support the hypothesis that superoxide radicals are involved in the chelator-mediated Fenton process. In the second part of the study, the effect of a chelator-mediated Fenton system for the improvement of deinking efficiency and the n~odification of fiber and paper properties was studied. For the deinking study, copy paper was laser printed with an identical standard pattern. Then repulping and flotation operations were performed to remove ink particles. Under properly controlled deinking conditions, the chelator mediated treatment (CMT) resulted in a reduction in dirt count over that of conventional deinking procedures with no significant loss of pulp strength. To study the effect of the chelator system treatment on the quality of pulp with different fines content, a fully bleached hardwood kraft pulp was beaten to different freeness levels and treated with the chelator-mediated free radical system. The result shows that virgin fiber and heavily beaten fiber respond differently to the free radical treatment. Unbeaten fibers become more flexible and easier to collapse after free radical treatment, while beaten fibers show a reduction in fines and small materials after mild free radical treatment.

On the improvement of urban regeneration processes from more than thirty years of rehabilitation experiences

At this moment of extended economic, social and environmental crisis within which new interventions on the consolidated city are being set out, it is essential to count on the acquired experience in urban rehabilitation processes that were carried out in Spain during the last thirty years. Despite the complexity of this kind of processes and the diversity of the situations and actions that happened, this paper addresses the analysis of common patterns in twenty urban rehabilitation experiences. Different stages of the processes were studied, from the management to the regenerated areas in order to ease the design of new intervention initiatives.

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.

Oil Flow Rate Measurements Using 198au And Total Count Technique

In industrial plants, oil and oil compounds are usually transported by closed pipelines with circular cross-section. The use of radiotracers in oil transport and processing industrial facilities allows calibrating flowmeters, measuring mean residence time in cracking columns, locate points of obstruction or leak in underground ducts, as well as investigating flow behavior or industrial processes such as in distillation towers. Inspection techniques using radiotracers are non-destructive, simple, economic and highly accurate. Among them, Total Count, which uses a small amount of radiotracer with known activity, is acknowledged as an absolute technique for flow rate measurement. A viscous fluid transport system, composed by four PVC pipelines with 13m length (12m horizontal and 1m vertical) and ½, ¾, 1 and 2-inch gauges, respectively, interconnected by maneuvering valves was designed and assembled in order to conduct the research. This system was used to simulate different flow conditions of petroleum compounds and for experimental studies of flow profile in the horizontal and upward directions. As 198Au presents a single photopeak (411,8 keV), it was the radioisotope chosen for oil labeling, in small amounts (6 ml) or around 200 kBq activity, and it was injected in the oil transport lines. A NaI scintillation detector 2”x 2”, with well-defined geometry, was used to measure total activity, determine the calibration factor F and, positioned after a homogenization distance and interconnected to a standardized electronic set of nuclear instrumentation modules (NIM), to detect the radioactive cloud.