Slope stability analysis of the Pacaya Volcano, Guatemala, using limit equilibrium and finite element method

The Pacaya volcanic complex is part of the Central American volcanic arc, which is associated with the subduction of the Cocos tectonic plate under the Caribbean plate. Located 30 km south of Guatemala City, Pacaya is situated on the southern rim of the Amatitlan Caldera. It is the largest post-caldera volcano, and has been one of Central America’s most active volcanoes over the last 500 years. Between 400 and 2000 years B.P, the Pacaya volcano had experienced a huge collapse, which resulted in the formation of horseshoe-shaped scarp that is still visible. In the recent years, several smaller collapses have been associated with the activity of the volcano (in 1961 and 2010) affecting its northwestern flanks, which are likely to be induced by the local and regional stress changes. The similar orientation of dry and volcanic fissures and the distribution of new vents would likely explain the reactivation of the pre-existing stress configuration responsible for the old-collapse. This paper presents the first stability analysis of the Pacaya volcanic flank. The inputs for the geological and geotechnical models were defined based on the stratigraphical, lithological, structural data, and material properties obtained from field survey and lab tests. According to the mechanical characteristics, three lithotechnical units were defined: Lava, Lava-Breccia and Breccia-Lava. The Hoek and Brown’s failure criterion was applied for each lithotechnical unit and the rock mass friction angle, apparent cohesion, and strength and deformation characteristics were computed in a specified stress range. Further, the stability of the volcano was evaluated by two-dimensional analysis performed by Limit Equilibrium (LEM, ROCSCIENCE) and Finite Element Method (FEM, PHASE 2 7.0). The stability analysis mainly focused on the modern Pacaya volcano built inside the collapse amphitheatre of “Old Pacaya”. The volcanic instability was assessed based on the variability of safety factor using deterministic, sensitivity, and probabilistic analysis considering the gravitational instability and the effects of external forces such as magma pressure and seismicity as potential triggering mechanisms of lateral collapse. The preliminary results from the analysis provide two insights: first, the least stable sector is on the south-western flank of the volcano; second, the lowest safety factor value suggests that the edifice is stable under gravity alone, and the external triggering mechanism can represent a likely destabilizing factor.

GENERATING PLANS IN CONCURRENT, PROBABILISTIC, OVER-SUBSCRIBED DOMAINS

Planning in realistic domains typically involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal subset of goals to work on. Creating optimal plans that consider all of these features is a computationally complex, challenging problem. This dissertation develops an AO* search based planner named CPOAO* (Concurrent, Probabilistic, Over-subscription AO*) which incorporates durative actions, time and resource constraints, concurrent execution, over-subscribed goals, and probabilistic actions. To handle concurrent actions, action combinations rather than individual actions are taken as plan steps. Plan optimization is explored by adding two novel aspects to plans. First, parallel steps that serve the same goal are used to increase the plan’s probability of success. Traditionally, only parallel steps that serve different goals are used to reduce plan execution time. Second, actions that are executing but are no longer useful can be terminated to save resources and time. Conventional planners assume that all actions that were started will be carried out to completion. To reduce the size of the search space, several domain independent heuristic functions and pruning techniques were developed. The key ideas are to exploit dominance relations for candidate action sets and to develop relaxed planning graphs to estimate the expected rewards of states. This thesis contributes (1) an AO* based planner to generate parallel plans, (2) domain independent heuristics to increase planner efficiency, and (3) the ability to execute redundant actions and to terminate useless actions to increase plan efficiency.

Cost-effectiveness of ticagrelor and generic clopidogrel in patients with acute coronary syndrome in Switzerland

QUESTION UNDER STUDY The aim of this study was to evaluate the cost-effectiveness of ticagrelor and generic clopidogrel as add-on therapy to acetylsalicylic acid (ASA) in patients with acute coronary syndrome (ACS), from a Swiss perspective. METHODS Based on the PLATelet inhibition and patient Outcomes (PLATO) trial, one-year mean healthcare costs per patient treated with ticagrelor or generic clopidogrel were analysed from a payer perspective in 2011. A two-part decision-analytic model estimated treatment costs, quality-adjusted life years (QALYs), life years and the cost-effectiveness of ticagrelor and generic clopidogrel in patients with ACS up to a lifetime at a discount of 2.5% per annum. Sensitivity analyses were performed. RESULTS Over a patient's lifetime, treatment with ticagrelor generates an additional 0.1694 QALYs and 0.1999 life years at a cost of CHF 260 compared with generic clopidogrel. This results in an Incremental Cost Effectiveness Ratio (ICER) of CHF 1,536 per QALY and CHF 1,301 per life year gained. Ticagrelor dominated generic clopidogrel over the five-year and one-year periods with treatment generating cost savings of CHF 224 and 372 while gaining 0.0461 and 0.0051 QALYs and moreover 0.0517 and 0.0062 life years, respectively. Univariate sensitivity analyses confirmed the dominant position of ticagrelor in the first five years and probabilistic sensitivity analyses showed a high probability of cost-effectiveness over a lifetime. CONCLUSION During the first five years after ACS, treatment with ticagrelor dominates generic clopidogrel in Switzerland. Over a patient's lifetime, ticagrelor is highly cost-effective compared with generic clopidogrel, proven by ICERs significantly below commonly accepted willingness-to-pay thresholds.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Integrated deterministic-probabilistic safety assessment methodologies

(ENG) IDPSA (Integrated Deterministic-Probabilistic Safety Assessment) is a family of methods which use tightly coupled probabilistic and deterministic approaches to address respective sources of uncertainties, enabling Risk informed decision making in a consistent manner. The starting point of the IDPSA framework is that safety justification must be based on the coupling of deterministic (consequences) and probabilistic (frequency) considerations to address the mutual interactions between stochastic disturbances (e.g. failures of the equipment, human actions, stochastic physical phenomena) and deterministic response of the plant (i.e. transients). This paper gives a general overview of some IDPSA methods as well as some possible applications to PWR safety analyses (SPA)DPSA (Metodologías Integradas de Análisis Determinista-Probabilista de Seguridad) es un conjunto de métodos que utilizan métodos probabilistas y deterministas estrechamente acoplados para abordar las respectivas fuentes de incertidumbre, permitiendo la toma de decisiones Informada por el Riesgo de forma consistente. El punto de inicio del marco IDPSA es que la justificación de seguridad debe estar basada en el acoplamiento entre consideraciones deterministas (consecuencias) y probabilistas (frecuencia) para abordar la interacción mutua entre perturbaciones estocásticas (como por ejemplo fallos de los equipos, acciones humanas, fenómenos físicos estocásticos) y la respuesta determinista de la planta (como por ejemplo los transitorios). Este artículo da una visión general de algunos métodos IDSPA así como posibles aplicaciones al análisis de seguridad de los PWR.

Scaling in geology: landforms and earthquakes.

Landforms and earthquakes appear to be extremely complex; yet, there is order in the complexity. Both satisfy fractal statistics in a variety of ways. A basic question is whether the fractal behavior is due to scale invariance or is the signature of a broadly applicable class of physical processes. Both landscape evolution and regional seismicity appear to be examples of self-organized critical phenomena. A variety of statistical models have been proposed to model landforms, including diffusion-limited aggregation, self-avoiding percolation, and cellular automata. Many authors have studied the behavior of multiple slider-block models, both in terms of the rupture of a fault to generate an earthquake and in terms of the interactions between faults associated with regional seismicity. The slider-block models exhibit a remarkably rich spectrum of behavior; two slider blocks can exhibit low-order chaotic behavior. Large numbers of slider blocks clearly exhibit self-organized critical behavior.

Simulation of air traffic controllers' behaviour using the operator choice model

The Operator Choice Model (OCM) was developed to model the behaviour of operators attending to complex tasks involving interdependent concurrent activities, such as in Air Traffic Control (ATC). The purpose of the OCM is to provide a flexible framework for modelling and simulation that can be used for quantitative analyses in human reliability assessment, comparison between human computer interaction (HCI) designs, and analysis of operator workload. The OCM virtual operator is essentially a cycle of four processes: Scan Classify Decide Action Perform Action. Once a cycle is complete, the operator will return to the Scan process. It is also possible to truncate a cycle and return to Scan after each of the processes. These processes are described using Continuous Time Probabilistic Automata (CTPA). The details of the probability and timing models are specific to the domain of application, and need to be specified using domain experts. We are building an application of the OCM for use in ATC. In order to develop a realistic model we are calibrating the probability and timing models that comprise each process using experimental data from a series of experiments conducted with student subjects. These experiments have identified the factors that influence perception and decision making in simplified conflict detection and resolution tasks. This paper presents an application of the OCM approach to a simple ATC conflict detection experiment. The aim is to calibrate the OCM so that its behaviour resembles that of the experimental subjects when it is challenged with the same task. Its behaviour should also interpolate when challenged with scenarios similar to those used to calibrate it. The approach illustrated here uses logistic regression to model the classifications made by the subjects. This model is fitted to the calibration data, and provides an extrapolation to classifications in scenarios outside of the calibration data. A simple strategy is used to calibrate the timing component of the model, and the results for reaction times are compared between the OCM and the student subjects. While this approach to timing does not capture the full complexity of the reaction time distribution seen in the data from the student subjects, the mean and the tail of the distributions are similar.

Wind power forecasts using Gaussian processes and numerical weather prediction

Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data. © 2013 IEEE.

Applications of Nonclassical Logic Methods for Purposes of Knowledge Discovery and Data Mining

* The work is partially supported by Grant no. NIP917 of the Ministry of Science and Education – Republic of Bulgaria.

A randomised controlled trial of the clinical effectiveness and cost-effectiveness of the levonorgestrel-releasing intrauterine system in primary care against standard treatment for menorrhagia:the ECLIPSE trial

BACKGROUND: Heavy menstrual bleeding (HMB) is a common problem, yet evidence to inform decisions about initial medical treatment is limited. OBJECTIVES: To assess the clinical effectiveness and cost-effectiveness of the levonorgestrel-releasing intrauterine system (LNG-IUS) (Mirena(®), Bayer) compared with usual medical treatment, with exploration of women's perspectives on treatment. DESIGN: A pragmatic, multicentre randomised trial with an economic evaluation and a longitudinal qualitative study. SETTING: Women who presented in primary care. PARTICIPANTS: A total of 571 women with HMB. A purposeful sample of 27 women who were randomised or ineligible owing to treatment preference participated in semistructured face-to-face interviews around 2 and 12 months after commencing treatment. INTERVENTIONS: LNG-IUS or usual medical treatment (tranexamic acid, mefenamic acid, combined oestrogen-progestogen or progesterone alone). Women could subsequently swap or cease their allocated treatment. OUTCOME MEASURES: The primary outcome was the patient-reported score on the Menorrhagia Multi-Attribute Scale (MMAS) assessed over a 2-year period and then again at 5 years. Secondary outcomes included general quality of life (QoL), sexual activity, surgical intervention and safety. Data were analysed using iterative constant comparison. A state transition model-based cost-utility analysis was undertaken alongside the randomised trial. Quality-adjusted life-years (QALYs) were derived from the European Quality of Life-5 Dimensions (EQ-5D) and the Short Form questionnaire-6 Dimensions (SF-6D). The intention-to-treat analyses were reported as cost per QALY gained. Uncertainty was explored by conducting both deterministic and probabilistic sensitivity analyses. RESULTS: The MMAS total scores improved significantly in both groups at all time points, but were significantly greater for the LNG-IUS than for usual treatment [mean difference over 2 years was 13.4 points, 95% confidence interval (CI) 9.9 to 16.9 points; p < 0.001]. However, this difference between groups was reduced and no longer significant by 5 years (mean difference in scores 3.9 points, 95% CI -0.6 to 8.3 points; p = 0.09). By 5 years, only 47% of women had a LNG-IUS in place and 15% were still taking usual medical treatment. Five-year surgery rates were low, at 20%, and were similar, irrespective of initial treatments. There were no significant differences in serious adverse events between groups. Using the EQ-5D, at 2 years, the relative cost-effectiveness of the LNG-IUS compared with usual medical treatment was £1600 per QALY, which by 5 years was reduced to £114 per QALY. Using the SF-6D, usual medical treatment dominates the LNG-IUS. The qualitative findings show that women's experiences and expectations of medical treatments for HMB vary considerably and change over time. Women had high expectations of a prompt effect from medical treatments. CONCLUSIONS: The LNG-IUS, compared with usual medical therapies, resulted in greater improvement over 2 years in women's assessments of the effect of HMB on their daily routine, including work, social and family life, and psychological and physical well-being. At 5 years, the differences were no longer significant. A similar low proportion of women required surgical intervention in both groups. The LNG-IUS is cost-effective in both the short and medium term, using the method generally recommended by the National Institute for Health and Care Excellence. Using the alternative measures to value QoL will have a considerable impact on cost-effectiveness decisions. It will be important to explore the clinical and health-care trajectories of the ECLIPSE (clinical effectiveness and cost-effectiveness of levonorgestrel-releasing intrauterine system in primary care against standard treatment for menorrhagia) trial participants to 10 years, by which time half of the cohort will have reached menopause. TRIAL REGISTRATION: Current Controlled Trials ISRCTN86566246. FUNDING: This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 19, No. 88. See the NIHR Journals Library website for further project information.

Probabilistic topographic information visualisation

The focus of this thesis is the extension of topographic visualisation mappings to allow for the incorporation of uncertainty. Few visualisation algorithms in the literature are capable of mapping uncertain data with fewer able to represent observation uncertainties in visualisations. As such, modifications are made to NeuroScale, Locally Linear Embedding, Isomap and Laplacian Eigenmaps to incorporate uncertainty in the observation and visualisation spaces. The proposed mappings are then called Normally-distributed NeuroScale (N-NS), T-distributed NeuroScale (T-NS), Probabilistic LLE (PLLE), Probabilistic Isomap (PIso) and Probabilistic Weighted Neighbourhood Mapping (PWNM). These algorithms generate a probabilistic visualisation space with each latent visualised point transformed to a multivariate Gaussian or T-distribution, using a feed-forward RBF network. Two types of uncertainty are then characterised dependent on the data and mapping procedure. Data dependent uncertainty is the inherent observation uncertainty. Whereas, mapping uncertainty is defined by the Fisher Information of a visualised distribution. This indicates how well the data has been interpolated, offering a level of ‘surprise’ for each observation. These new probabilistic mappings are tested on three datasets of vectorial observations and three datasets of real world time series observations for anomaly detection. In order to visualise the time series data, a method for analysing observed signals and noise distributions, Residual Modelling, is introduced. The performance of the new algorithms on the tested datasets is compared qualitatively with the latent space generated by the Gaussian Process Latent Variable Model (GPLVM). A quantitative comparison using existing evaluation measures from the literature allows performance of each mapping function to be compared. Finally, the mapping uncertainty measure is combined with NeuroScale to build a deep learning classifier, the Cascading RBF. This new structure is tested on the MNist dataset achieving world record performance whilst avoiding the flaws seen in other Deep Learning Machines.

Predicting dyslexia using prereading skills:the role of sensorimotor and cognitive abilities

Background: It is well established that phonological awareness, print knowledge and rapid naming predict later reading difficulties. However, additional auditory, visual and motor difficulties have also been observed in dyslexic children. It is examined to what extent these difficulties can be used to predict later literacy difficulties. Method: An unselected sample of 267 children at school entry completed a wide battery of tasks associated with dyslexia. Their reading was tested 2, 3 and 4 years later and poor readers were identified (n = 42). Logistic regression and multiple case study approaches were used to examine the predictive validity of different tasks. Results: As expected, print knowledge, verbal short-term memory, phonological awareness and rapid naming were good predictors of later poor reading. Deficits in visual search and in auditory processing were also present in a large minority of the poor readers. Almost all poor readers showed deficits in at least one area at school entry, but there was no single deficit that characterised the majority of poor readers. Conclusions: Results are in line with Pennington’s (2006) multiple deficits view of dyslexia. They indicate that the causes of poor reading outcome are multiple, interacting and probabilistic, rather than deterministic. Keywords: Dyslexia; educational attainment; longitudinal studies; prediction; phonological processing.

On Probabilistic Rationing Methods

Rationing occurs if the demand for a certain good exceeds its supply. In such situations a rationing method has to be specified in order to determine the allocation of the scarce good to the agents. Moulin (1999) introduced the notion of probabilistic rationing methods for the discrete framework. In this paper we establish a link between classical and probabilistic rationing methods. In particular, we assign to any given classical rationing method a probabilistic rationing method with minimal variance among those probabilistic rationing methods, which result in the same expected distributions as the given classical rationing method.

An aquatic risk assessment of ethion and bromacil inputs to the C-25 Canal and Indian River Lagoon

Pesticide monitoring in St. Lucie County by various local, state and federal agencies has indicated consistent residues of several pesticides, including ethion and bromacil. Although pesticides have long been known to pose a threat to non-target species and much background monitoring has been done, no pesticide aquatic risk assessment has been done in this geographical area. Several recognized United States Environmental Protection Agency (USEPA) methods of quantifying risk are employed here to include hazard quotients (HQ) and probabilistic modeling with sensitivity analysis. These methods are employed to characterize potential impacts to aquatic biota of the C-25 Canal and the Indian River Lagoon (in St. Lucie County, Florida) based on current agricultural pesticide use and drainage patterns. The model used in the analysis incorporates available physical-chemical property data, local hydrology, ecosystem information, and pesticide use practices. HQ's, probabilistic distributions, and field sample analyses resulted in high levels of concern (LOCs), which usually indicates a need for regulatory action, including restrictions on use, or cancellation. ^

A probabilistic approach to the estimation of regional photovoltaic power generation using meteorological data

In Germany the upscaling algorithm is currently the standard approach for evaluating the PV power produced in a region. This method involves spatially interpolating the normalized power of a set of reference PV plants to estimate the power production by another set of unknown plants. As little information on the performances of this method could be found in the literature, the first goal of this thesis is to conduct an analysis of the uncertainty associated to this method. It was found that this method can lead to large errors when the set of reference plants has different characteristics or weather conditions than the set of unknown plants and when the set of reference plants is small. Based on these preliminary findings, an alternative method is proposed for calculating the aggregate power production of a set of PV plants. A probabilistic approach has been chosen by which a power production is calculated at each PV plant from corresponding weather data. The probabilistic approach consists of evaluating the power for each frequently occurring value of the parameters and estimating the most probable value by averaging these power values weighted by their frequency of occurrence. Most frequent parameter sets (e.g. module azimuth and tilt angle) and their frequency of occurrence have been assessed on the basis of a statistical analysis of parameters of approx. 35 000 PV plants. It has been found that the plant parameters are statistically dependent on the size and location of the PV plants. Accordingly, separate statistical values have been assessed for 14 classes of nominal capacity and 95 regions in Germany (two-digit zip-code areas). The performances of the upscaling and probabilistic approaches have been compared on the basis of 15 min power measurements from 715 PV plants provided by the German distribution system operator LEW Verteilnetz. It was found that the error of the probabilistic method is smaller than that of the upscaling method when the number of reference plants is sufficiently large (>100 reference plants in the case study considered in this chapter). When the number of reference plants is limited (<50 reference plants for the considered case study), it was found that the proposed approach provides a noticeable gain in accuracy with respect to the upscaling method.