Análise de incertezas do modelo de teores associado aos investimentos de pesquisa de longo prazo

Os empreendimentos de mineração comumente demandam grande quantidade de investimentos financeiros e, na maioria das vezes, longos períodos de implantação, o que os torna altamente sujeitos a diversas fontes de incertezas. Tais incertezas comumente tendem a diminuir conforme a evolução do projeto. O objetivo deste estudo é correlacionar as incertezas associadas ao modelo de teores de cobre do depósito Sequeirinho com o volume de investimentos realizados ao longo de distintas fases da pesquisa geológica. Este depósito insere-se no contexto do Complexo de Mineração Sossego, localizado no município de Canaã dos Carajás (PA). Primeiramente, foram realizadas 100 simulações para cada domínio litológico em cada campanha de sondagem (pré-1998, 1999, 2000, 2002 e 2003) a partir do método de simulação sequencial gaussiana condicionada aos dados amostrais, totalizando 1.400 possíveis cenários. Para a avaliação das incertezas foram calculados três índices: variância condicional, coeficiente de variação condicional e intervalo interquartil. Por fim, a avaliação dos investimentos foi elaborada a partir dos custos estimados para o desenvolvimento de sondagens e análises químicas. Desde a campanha pré-1998, houve uma tendência de os teores médios do depósito aproximarem-se dos prováveis valores reais observados nas fases finais da pesquisa. No ano de 2000 ocorreu o maior investimento (cerca de 28 milhões de Reais) e a redução das incertezas atingiu o patamar de 15%. Os investimentos desenvolvidos em sondagens posteriores à campanha de 2000 foram da ordem de 9 milhões de Reais (cerca de 12 mil metros de sondagem), porém, não foram constatadas reduções significativas das incertezas. Este investimento seria melhor aproveitado caso fosse redirecionado a novas áreas de prospecção. Além do montante financeiro necessário para a redução das incertezas, foco deste estudo, as variações na interpretação geológica e a locação dos furos de sondagem são variáveis importantes na análise de incertezas associadas aos investimentos em pesquisa geológica.

Assessing cost-effectiveness of drug interventions for schizophrenia

Objective: To assess from a health sector perspective the incremental cost-effectiveness of eight drug treatment scenarios for established schizophrenia. Method: Using a standardized methodology, costs and outcomes are modelled over the lifetime of prevalent cases of schizophrenia in Australia in 2000. A two-stage approach to assessment of health benefit is used. The first stage involves a quantitative analysis based on disability-adjusted life years (DALYs) averted, using best available evidence. The robustness of results is tested using probabilistic uncertainty analysis. The second stage involves application of 'second filter' criteria (equity, strength of evidence, feasibility and acceptability) to allow broader concepts of benefit to be considered. Results: Replacing oral typicals with risperidone or olanzapine has an incremental cost-effectiveness ratio (ICER) of A$48 000 and A$92 000/DALY respectively. Switching from low-dose typicals to risperidone has an ICER of A$80 000. Giving risperidone to people experiencing side-effects on typicals is more cost-effective at A$20 000. Giving clozapine to people taking typicals, with the worst course of the disorder and either little or clear deterioration, is cost-effective at A$42 000 or A$23 000/DALY respectively. The least cost-effective intervention is to replace risperidone with olanzapine at A$160 000/DALY. Conclusions: Based on an A$50 000/DALY threshold, low-dose typical neuroleptics are indicated as the treatment of choice for established schizophrenia, with risperidone being reserved for those experiencing moderate to severe side-effects on typicals. The more expensive olanzapine should only be prescribed when risperidone is not clinically indicated. The high cost of risperidone and olanzapine relative to modest health gains underlie this conclusion. Earlier introduction of clozapine however, would be cost-effective. This work is limited by weaknesses in trials (lack of long-term efficacy data, quality of life and consumer satisfaction evidence) and the translation of effect size into a DALY change. Some stakeholders, including SANE Australia, argue the modest health gains reported in the literature do not adequately reflect perceptions by patients, clinicians and carers, of improved quality of life with these atypicals.

Development of an integrated surface and subsurface model of Everglades National Park

An integrated surface-subsurface hydrological model of Everglades National Park (ENP) was developed using MIKE SHE and MIKE 11 modeling software. The model has a resolution of 400 meters, covers approximately 1050 square miles of ENP, includes 110 miles of drainage canals with a variety of hydraulic structures, and processes hydrological information, such as evapotranspiration, precipitation, groundwater levels, canal discharges and levels, and operational schedules. Calibration was based on time series and probability of exceedance for water levels and discharges in the years 1987 through 1997. Model verification was then completed for the period of 1998 through 2005. Parameter sensitivity in uncertainty analysis showed that the model was most sensitive to the hydraulic conductivity of the regional Surficial Aquifer System, the Manning's roughness coefficient, and the leakage coefficient, which defines the canal-subsurface interaction. The model offers an enhanced predictive capability, compared to other models currently available, to simulate the flow regime in ENP and to forecast the impact of topography, water flows, and modifying operation schedules.

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Modelling and Mining To Manage

In this talk, I will describe various computational modelling and data mining solutions that form the basis of how the office of Deputy Head of Department (Resources) works to serve you. These include lessons I learn about, and from, optimisation issues in resource allocation, uncertainty analysis on league tables, modelling the process of winning external grants, and lessons we learn from student satisfaction surveys, some of which I have attempted to inject into our planning processes.

Quantifying the Resilience of an Urban Traffic-Electric Power Coupled System

Transportation system resilience has been the subject of several recent studies. To assess the resilience of a transportation network, however, it is essential to model its interactions with and reliance on other lifelines. In this work, a bi-level, mixed-integer, stochastic program is presented for quantifying the resilience of a coupled traffic-power network under a host of potential natural or anthropogenic hazard-impact scenarios. A two-layer network representation is employed that includes details of both systems. Interdependencies between the urban traffic and electric power distribution systems are captured through linking variables and logical constraints. The modeling approach was applied on a case study developed on a portion of the signalized traffic-power distribution system in southern Minneapolis. The results of the case study show the importance of explicitly considering interdependencies between critical infrastructures in transportation resilience estimation. The results also provide insights on lifeline performance from an alternative power perspective.

Cloud Base Height Measurement System Based on Stereo Vision with Automatic Calibration

Clouds are important in weather prediction, climate studies and aviation safety. Important parameters include cloud height, type and cover percentage. In this paper, the recent improvements in the development of a low-cost cloud height measurement setup are described. It is based on stereo vision with consumer digital cameras. The cameras positioning is calibrated using the position of stars in the night sky. An experimental uncertainty analysis of the calibration parameters is performed. Cloud height measurement results are presented and compared with LIDAR measurements.

Multicriteria analysis in decision making under information uncertainty

This paper presents results of research related to multicriteria decision making under information uncertainty. The Bell-man-Zadeh approach to decision making in a fuzzy environment is utilized for analyzing multicriteria optimization models (< X, M > models) under deterministic information. Its application conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analyzing associated maxmin problems. This circumstance permits one to generalize the classic approach to considering the uncertainty of quantitative information (based on constructing and analyzing payoff matrices reflecting effects which can be obtained for different combinations of solution alternatives and the so-called states of nature) in monocriteria decision making to multicriteria problems. Considering that the uncertainty of information can produce considerable decision uncertainty regions, the resolving capacity of this generalization does not always permit one to obtain unique solutions. Taking this into account, a proposed general scheme of multicriteria decision making under information uncertainty also includes the construction and analysis of the so-called < X, R > models (which contain fuzzy preference relations as criteria of optimality) as a means for the subsequent contraction of the decision uncertainty regions. The paper results are of a universal character and are illustrated by a simple example. (c) 2007 Elsevier Inc. All rights reserved.

Mathematical aspects of the Heisenberg uncertainty principle within local fractional Fourier analysis

In this paper, we discuss the mathematical aspects of the Heisenberg uncertainty principle within local fractional Fourier analysis. The Schrödinger equation and Heisenberg uncertainty principles are structured within local fractional operators.

Optimal scheduling with uncertainty in the numerical data on the basis of a stability analysis

Scheduling, job shop, uncertainty, mixed (disjunctive) graph, stability analysis

Bidding at sequential first-price auctions with(out) supply uncertainty: a laboratory analysis

We report on a series of experiments that test the effects of an uncertain supply on the formation of bids and prices in sequential first-price auctions with private-independent values and unit-demands. Supply is assumed uncertain when buyers do not know the exact number of units to be sold (i.e., the length of the sequence). Although we observe a non-monotone behavior when supply is certain and an important overbidding, the data qualitatively support our price trend predictions and the risk neutral Nash equilibrium model of bidding for the last stage of a sequence, whether supply is certain or not. Our study shows that behavior in these markets changes significantly with the presence of an uncertain supply, and that it can be explained by assuming that bidders formulate pessimistic beliefs about the occurrence of another stage.

Price competition under cost uncertainty: A laboratory analysis

We study the relation between the number of firms and price-cost margins under price competition with uncertainty about competitors' costs. We present results of an experiment in which two, three and four identical firms repeatedly interact in this environment. In line with the theoretical prediction, market prices decrease with the number of firms, but on average stay above marginal costs. Pricing is less aggressive in duopolies than in triopolies and tetrapolies. However, independently from the number of firms, pricing is more aggressive than in the theoretical equilibrium. Both the absolute and the relative surpluses increase with the number of firms. Total surplus is close to the equilibrium level, since enhanced consumer surplus through lower prices is counteracted by occasional displacements of the most efficient firm in production.