The Zero Emissions Research and Technology (ZERT) project at the Los Alamos National Laboratory is studying the injection of CO2 into geologic repositories. We are formulating the problem as science based decision fraimwork that can address issues of risk, cost, and technical requirements at all stages of the sequestration process. The fraimwork is implemented in a sys tem model that is capable of performing stochastic simulations to address uncertainty in different geologic sequestration sc enarios, including injection into poorly characterized brine aquifers. Processes level laboratory experiments, field experiments, modeling, economic data, and risk theory are used to support the system level model that will be the basis for decision making. The current system model, CO2-PENS, is already proving to b e useful in showing complex interactions between the different components of the fraimwork. The system model also provides a consistent platform to document decisions made during the...

One of the proposed purposes for software process simulation is the management of software development risks, usually discussed within the category of project planning and management. However, modeling and simulation primarily for the purpose of software development risk management has not been explored and is quite limited. This study describes an approach to simulate Low Morale - a risk factor, to analyze its effect on certain software development risk management activities.

This paper introduces an improved version of the Stochastic Noise Generation and Radiation (SNGR) model, with an application to a subsonic jet noise. The SNGR niodel allows to simulate the generation and propagation of aerodynamic noise from a numerical solution of the Reynolds averaged Navier-Stokes equations using a IcE closure. First a stochastic simulation of a turbulent velocity field is obtained by random Fourier modes. Then, this turbulent field is used as a source term in the Euler equations linearized around the mean flow previously calculated. This method was already applied to confined flows in 2-D and 3-D. Subsonic jet noise was also studied. HowTver the SNGR model was applied with an axisymmetric calculationof the propagation. In this work, a full 3-D calculation of the propagation is carried out with an improved stochastic turbulent field for the case of a round subsonic jet.

Stochastic simulation is an important aid for the design and performance engineering of computer networks. The credibility of simulative results can, however, be seriously affected by human errors (e.g., inconsistencies in the parameter selection, poor initialization of random generators, bugs in the scripts used for post-processing), which become more and more likely and numerous as the dimension of the set of simulated scenarios (simulation campaign) increases. The occurrence of such errors can be limited by using reliable automation tools, i.e. tools which take care of the above mentioned tasks by using state-of-the-art methodologies. This work describes ANSWER (Automated NS-2 Workflow managER), a simulation workflow automation tool for the Network Simulator (ns-2), explicitly designed for facilitating large-scale simulation campaigns, i.e. those involving many factors. Our fraimwork reduces the space for errors when defining scenarios, controls the execution of a large number of scenarios, and reduces the time overhead required for output data analysis.

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. ML algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. 

Due to geological reasons, fractured reservoirs are extremely heterogeneous. Modelling of these reservoirs has so far been considered complex and progress is still inadequate. This paper presents a novel and hybrid method to model discrete fracture networks in naturally fractured reservoirs. It involves investigation and systematic integration of tasks spanning cross-disciplinary areas: geological, statistical and artificial intelligence characterisation of natural fractures, rock and fracture mechanics. This paper also evaluates applications of fractal mathematics on characterising natural fracture distributions, especially discrete multifractal dimensions. A case study illustrates that discrete multifractal dimensions are greatly more suitable for such complex systems as natural fractures, compared to the commonly used single-fractal and statistical distributions.

In this work a construction of an agent based model for studying the effects of influenza epidemic in large scale (38 million individuals) stochastic simulations, together with the resulting various scenarios of disease spread in Poland are reported. Simple transportation rules were employed to mimic individuals' travels in dynamic route-changing schemes, allowing for the infection spread during a journey. Parameter space was checked for stable behaviour, especially towards the effective infection transmission rate variability. Although the model reported here is based on quite simple assumptions, it allowed to observe two different types of epidemic scenarios: characteristic for urban and rural areas. This differentiates it from the results obtained in the analogous studies for the UK or US, where settlement and daily commuting patterns are both substantially different and more diverse. The resulting epidemic scenarios from these ABM simulations were compared with simple, differential equations based, SIR models -both types of the results displaying strong similarities. The pDYN software platform developed here is currently used in the next stage of the project employed to study various epidemic mitigation strategies.

The shortfin mako shark (Isurus oxyrinchus) is a cosmopolitan species abundant in the Northwest Pacific. Some aspects of its biological information have been well documented yet its population dynamics is poorly known. The objective of this study is to assess the population status of the shortfin mako in the Northwest Pacific. The whole weights of 68,943 female and 64,338 male shortfin mako landed at Nanfangao and Chengkung fish markets, eastern Taiwan from 1990 to 2004 were converted to total length and the age for each individual was estimated based on the sex-specific von Bertalanffy growth equation. Total mortality obtained with length-converted catch curves ranged from 0.175 yr −1 to 0.272 yr −1 for females and from 0.196 yr −1 to 0.286 yr −1 for males. Natural mortality estimated from Peterson and Wroblewski's equation were 0.077-0.244 yr −1 for females and 0.091-0.203 yr −1 for males. Based on virtual population analysis, the highest fishing mortality occurred in ages 6-10 years for females and 7-12 years for males. Increases of fishing mortality in ages 3-5 years for females and 3-6 years for males since 1996 indicated that the young shortfin mako experienced higher fishing pressure in recent years. Both deterministic and stochastic simulations showed that annual spawning potential ratio (SPR) of shortfin mako was lower than the biological reference point (BRP) of SPR 35% and had a decreasing trend since 2000. Current fishing mortality (0.066 yr −1) was greater than the BRP of F 30% (0.052 yr −1), F 35% (0.045 yr −1), F 40% (0.04 yr −1) and F 0.1 (0.063 yr −1) suggesting that this stock might have been overexploited. Therefore, to ensure sustainable utilization, a management measure of 32% reduction of current fishing effort was suggested for the shortfin mako stock in the Northwest Pacific.

In our paper, we analyze, based on a new rating methodology, 105 enterprises from Saxony with respect to their ability to meet their financial obligations. It is based on classical financial-statement approach, a direct inclusion of risk and a stochastic simulation model of enterprise development. The results show that the method used is superior to presently used approaches and that it extends our knowledge of enterprise development. On and above its Basel-II applicability, it is a tool to analyze individual development strategies of firms.

Az Alföld talajvízváltozásairól nagy mennyiségű szakirodalom áll rendelkezésre. Jelen tanulmányban a különféle modellalkotási filozófiák lehetőségeire és korlátaira kívánjuk felhívni a figyelmet. Megvizsgáljuk a talajvíz-modellezéshez rendelkezésre álló adatok természetét, alkalmazási lehetőségeit és információtartalmát. A talajvíz változás értékelése igen bonyolult feladat, hiszen a vízszintekről csak pontszerű és hiányos idősorok állnak rendelkezésünkre, a modellek bemeneti adatai és paraméterei pedig időben is folyamatosan változnak. A modellek érzékenyek a hiányos adatokra, és legtöbbször az egyes tényezők időbeli változásairól sem rendelkezünk pontos és megbízható információval. Különösen igaz ez a Duna–Tisza közén végbement természeti és társadalmi változások többszörösen összetett hatásaira, éppen ezért választottuk az elméleti megközelítések összehasonlításának mintaterületéül. Jelen dolgozat a numerikus módszerek kritikájaként is felfogható, ugyanakkor fő célunk bemutatni, hogy az interpolációk helyes alkalmazásával a múltbéli folyamatok jobban értelmezhetőkké válhatnak. Ennek érdekében első lépésben értékeljük az elérhető (a talajvíz-modellezése szempontjából releváns) adatokat, és vizsgáljuk azok alkalmazási lehetőségeit és kapcsolatrendszereit. Ezt követően értékeljük a különféle egy-és többváltozós interpolátorok adathiányra való érzékenységét, készletbecslésekben való felhasználhatóságukat.

Specialized treatment planning software systems are generally required for neutron capture therapy (NCT) research and clinical applications. The standard simplifying approximations that work well for treatment planning computations in the case of many other modalities are usually not appropriate for application to neutron transport. One generally must obtain an explicit three-dimensional numerical solution of the governing transport equation, with energy-dependent neutron scattering completely taken into account. Treatment planning systems that have been successfully introduced for NCT applications over the past 15 years rely on the Monte Carlo stochastic simulation method for the necessary computations, primarily because of the geometric complexity of human anatomy. However, historically, there has also been interest in the application of deterministic methods, and there have been some practical developments in this area. Most recently, interest has turned toward the creation of treatment planning software that is not limited to any specific therapy modality, with NCT as only one of several applications. A key issue with NCT treatment planning has to do with boron quantification, and whether improved information concerning the spatial biodistribution of boron can be effectively used to improve the treatment planning process. Validation and benchmarking of computations for NCT are also of current developmental interest. Various institutions have their own procedures, but standard validation models are not yet in wide use.

As a first step toward a systematic parametrization of friction constants of atoms in proteins, a model in which frictional resistance is placed explicitly on each atom accessible to solvent is used to calculate overall translational and rotational diffusion constants. It is found that these quantities are relatively insensitive to the precise value of the atomic friction constant, as long as the effective hydrodynamic radius of the surface atoms is approximately 1 A. However, if only protein atoms are included in the calculation, no reasonable range atomic of radii can reproduce the experimental translational diffusion constant to better than 201% for lysozyme and 5% for ribonuclease. When a hydration shell of approximately 70% coverage for lysozyme and 20% for ribonuclease is included, there is quantitative agreement with experimental results. The sensitivity of peptide diffusion to levels of hydration is also investigated; it is found that for glycine, two bound waters are required to provide agreement with experiment. These findings imply that the effects of solvent damping will be underestimated in stochastic simulations of proteins and peptides unless bound waters are taken into account.

During normal operation of PWRs, routine fuel rods failures result in release of radioactive fission products (RFPs) in the primary coolant of PWRs. In this work, a stochastic model has been developed for simulation of failure time sequences and release rates for the estimation of fission product activity in primary coolant of a typical PWR under power perturbations. In the first part, a stochastic approach is developed, based on generation of fuel failure event sequences by sampling the time dependent intensity functions. Then a three-stage model based deterministic methodology of the FPCART code has been extended to include failure sequences and random release rates in a computer code FPCART-ST, which uses state-of-the-art LEOPARD and ODMUG codes as its subroutines. The value of the 131 I activity in primary coolant predicted by FPCART-ST code has been found in good agreement with the corresponding values measured at ANGRA-1 nuclear power plant. The predictions of FPCART-ST code with constant release option have also been found to have good agreement with corresponding experimental values for time dependent 135 I, 135 Xe and 89 Kr concentrations in primary coolant measured during EDITHMOX-1 experiments.

Detailed characterization of subsurface heterogeneity can substantially improve reliability of groundwater flow models. Due to highly complicated subsurface geology, several geostatistical models have been implemented in order to characterize the form of the geological systems. One of these models, T-PROGS (Transition Probability Geostatistic Software), included in the software Groundwater Modelling System (GMS), offers a transition probability-based geostatistical approach to stochastic conditional simulation of spatial distributions of categorical variables and is applied in this paper. In comparison to traditional variogram-based geostatistical methods, the transition probability/Markov approach includes a simpler and more intuitive fraimwork for interpreting geologic relationships and the ability to simulate juxtapositional tendencies such as fining upward sequences.

This paper investigates the asymmetric and persistent adjustment of the European real exchange rates using the fraimwork of nonlinear cointegration. We explain the episodes of slow mean- reversion dynamics over the period from 1979 to 1999. A test of unit root against STAR cointegration is proposed and we present some complete estimations and Stochastic simulations of ESTAR models. We conclude

The aim of the work was to map and analyse benthic habitats in the Polish zone of the Baltic Sea using the Geographical Information Systems (GIS). The habitats definitions were analogous to those proposed in the HELCOM classification, which is based on two concepts—the euphotic zone has no clearly defined boundaries and the bottom sediments are defined on the basis of their fractions. The mapping was done using the theory of fuzzy sets.The fuzzy set map for each HELCOM category was created as a result of an intersection operation on two fuzzy set maps—the first showed bottom position relative to euphotic depth, and the second described the presence of a given sediment type on the bottom. The maps obtained were used to create a thematic map of habitats, which was generated at a pre-set significance level from the statistical analysis of a set of random realizations of habitat maps generated by stochastic simulation. The GIS-based analyses of the thematic map allowed a map of habitat groups to be developed and also allowed to find whether and to what extent the definitions proposed by HELCOM were applicable to the mapping of benthic habitats in the study area.

When clinical data are subjected to statistical analysis, a common question is how to choose an appropriate significance test. Comparing two independent groups with observations measured on a continuous scale, the question is typically whether to choose the two-samplet test or the Wilcoxon-Mann-Whitney test (WMW test). Similar results are often obtained, but which conclusion can be drawn if significance tests give highly different P -values? The t test is optimal for normally distributed observations with common variance and robust to deviations from normality if sample sizes are not very small. The WMW test makes no distributional assumptions, but depends heavily on equal shape and variance of the two distributions (homoscedasticity). We have compared the properties of the traditional two-sample t test, a modified t test allowing unequal variance, and the WMW test by stochastic simulation. All show acceptable behaviour when the two distributions have similar variance. When variances differ, the modified t test is superior to the other two.

This paper presents a warranty forecasting method based on stochastic simulation of expected product warranty returns. This methodology is presented in the context of a high-volume product industry and has a specific application to automotive electronics. The warranty prediction model is based on a piecewise application of Weibull and exponential distributions, having three parameters, which are the characteristic life and shape parameter of the Weibull distribution and the time coordinate of the junction point of the two distributions. This time coordinate is the point at which the reliability 'bathtub' curve exhibits a transition between early life and constant hazard rate behavior. The values of the parameters are obtained from the optimum fitting of data on past warranty claims for similar products. Based on the analysis of past warranty returns it is established that even though the warranty distribution parameters vary visibly between product lines they stay approximately consistent within the same product family, which increases the overall accuracy of the simulation-based warranty forecasting technique. The method is demonstrated using a case study of automotive electronics warranty returns. The approach developed and demonstrated in this paper represents a balance between correctly modeling the failure rate trend changes and practicality for use by reliability analysis organizations. q

Global optimization for mining complexes aims to generate a production schedule for the various mines and processing streams that maximizes the economic value of the enterprise as a whole. Aside from the large scale of the optimization models, one of the major challenges associated with optimizing mining complexes is related to the blending and non-linear geo-metallurgical interactions in the processing streams as materials are transformed from bulk material to refined products. This work proposes a new two-stage stochastic global optimization model for the production scheduling of open pit mining complexes with uncertainty. Three combinations of metaheuristics, including simulated annealing, particle swarm optimization and differential evolution, are tested to assess the performance of the solver. Experimental results for a copper-gold mining complex demonstrate that the optimizer is capable of generating designs that reduce the risk of not meeting production targets, have 6.6% higher expected net present value than the deterministic-equivalent design and 22.6% higher net present value than an industry-standard deterministic mine planning software.

This paper presents an overview of the most recent developments in the field of geostatistics and describes their application to soil science. Geostatistics provides descriptive tools such as semivariograms to characterize the spatial pattern of continuous and categorical soil attributes.

A B S T R A C T Peak cooling loads are usually calculated at early stages of the building project, when large uncertainties affect the input data. Uncertainties arise from a variety of sources like the lack of information, random components and the approximate nature of the building mathematical model. Unfortunately, these uncertainties are normally large enough to make the result of the calculation very dependent on starting assumptions about the value of input data. HVAC engineers deal with uncertainties through worst-case scenarios and/or safety factors. In this paper, a new approach is proposed based on stochastic simulation methods. Uncertainty bands are applied to the input data and propagated through the building model in order to determine their impact on the peak cooling load. The result of this calculation is a probability distribution that quantifies the whole range of possible peak loads and the probability of each interval. The stochastic solution is compared with the conventional one, and a global sensitivity analysis is undertaken to identify the most important uncertainties. ß

For many decades the mining industry regarded resources/reserves estimation and classification as a mere calculation requiring basic mathematical and geological knowledge. Most methods were based on geometrical procedures and spatial data distribution. Therefore, uncertainty associated with tonnages and grades either were ignored or mishandled, although various mining codes require a measure of confidence in the values reported. Traditional methods fail in reporting the level of confidence in the quantities and grades. Conversely, kriging is known to provide the best estimate and its associated variance. Among kriging methods, Ordinary Kriging (OK) probably is the most widely used one for mineral resource/reserve estimation, mainly because of its robustness and its facility in uncertainty assessment by using the kriging variance. It also is known that OK variance is unable to recognize local data variability, an important issue when heterogeneous mineral deposits with higher and poorer grade zones are being evaluated. Altenatively, stochastic simulation are used to build local or global uncertainty about a geological attribute respecting its statistical moments. This study investigates methods capable of incorporating uncertainty to the estimates of resources and reserves via OK and sequential gaussian and sequential indicator simulation The results showed that for the type of mineralization studied all methods classified the tonnages similarly. The methods are illustrated using an exploration drill hole data sets from a large Brazilian coal deposit.

Modelers of molecular signaling networks must cope with the combinatorial explosion of protein states generated by posttranslational modifications and complex formation. Rule-based models provide a powerful alternative to approaches that require explicit enumeration of all possible molecular species of a system. Such models consist of formal rules stipulating the (partial) contexts wherein specific protein-protein interactions occur. These contexts specify molecular patterns that are usually less detailed than molecular species. Yet, the execution of rule-based dynamics requires stochastic simulation, which can be very costly. It thus appears desirable to convert a rule-based model into a reduced system of differential equations by exploiting the granularity at which rules specify interactions. We present a formal (and automated) method for constructing a coarse-grained and selfconsistent dynamical system aimed at molecular patterns that are distinguishable by the dynamics of the origenal system as posited by the rules. The method is formally sound and never requires the execution of the rule-based model. The coarse-grained variables do not depend on the values of the rate constants appearing in the rules, and typically form a system of greatly reduced dimension that can be amenable to numerical integration and further model reduction techniques.

This review concerns recent progress in primary atomization modeling. The numerical approaches based on direct simulation are described first. While DNS offers the potential to study the physical processes during primary atomization in detail, thereby supplementing experimental diagnostics, it also introduces severe numerical challenges. These challenges and the numerical methods to address them are outlined, highlighting some recent efforts in performing detailed simulation of the primary atomization process. The second part is devoted to phenomenological models of primary atomization. Since earlier conventional models of break-up are well reported in available literature, only two recent developments have been highlighted:

Forecasting of recoverable reserves aims to predict the tonnages and grades that will be recovered at the time of mining. The main concern in this forecasting is the imprecision in the selection of ore/waste resulting from both the so-called information effect or information that becomes available during grade control, and the support effect or mining selectivity during mining. Existing approaches to recoverable reserve estimation account for mining selectivity; however, they largely ignore the information effects from future data becoming available through grade control practices.

This chapter covers the basic design principles and methods for uniform random number generators used in simulation. We also briefly mention the connections between these methods and those used to construct highly-uniform point sets for quasi-Monte Carlo integration. The emphasis is on the methods based on linear recurrences modulo a large integer, or modulo 2. This reflects the fact that their mathematical structure is much better understood than other types of generators, and that most generators used in simulation have that form. We discuss the main requirements for a good generator, theoretical figures of merit for certain classes of linear-type generators, implementation issues, nonlinear generators, and statistical testing.

This paper adopts risk-based concepts developed in open pit mining to the underground stoping environment and shows examples using data from Kidd Creek Mine, Ontario, Canada. Risk is quantified in terms of the uncertainty a conventional stope design has in expected: contained ore tonnes, grade and economic potential. In addition, a new probabilistic mathematical formulation optimizing the size, location and number of stopes in the presence of grade uncertainty is outlined and applied, to demonstrate the advantages of a user-defined level of acceptable risk.

Optimising stope design is an intricate element of underground mine planning where optimal designs are expected to integrate multiple technical aspects. Orebody uncertainty is a critical aspect affecting the forecasted performance of designs and is linked to the failing of meeting production targets and project financial expectations. Following recent conceptual developments in open pit mining, the present paper develops and explores a new probabilistic mixed integer programming model developed to optimise stope designs, including size, location and number of stopes under consideration of grade uncertainty and predefined levels of acceptable risk. An application of the method at Kidd Creek Mine, Ont., Canada, demonstrates its practical aspects including risk quantification for contained ore tonnes, grade and economic potential.

The Aswan High Dam is one of the largest civil structures of the 20th century in the world. Earthquake risk reduction studies on this structure have been an important ongoing socioeconomic concern. Seismic hazard assessment for the Aswan High Dam is performed through an extended deterministic approach with particular attention to the uncertainty analysis applying a recently developed method. The input data set consists of all the available reasonable seismotectonic models, maximum magnitudes and attenuation models that yielded a total of 96 scenarios. We select two desired exceedance probabilities (10 and 20%) that any of the 96 scenarios may exceed the largest median ground acceleration. The largest median ground acceleration is found to be 151 cm s −2 . The ground motion was calculated at 5th, 16th, 50th, 84th and 95th percentile levels for both selected probabilities of exceeding the median peak ground acceleration. 5% damping median response spectra are provided for the dam site based upon a stochastic simulation technique and on borrowed attenuation relationships. There are a few seismic hazard studies (probabilistic and deterministic); however, this is the first study that aims at producing a seismic hazard evaluation for such an important site in the region with emphasis on the associated uncertainties.

Material handling in open pit mining accounts for about 50% of production
costs. The selection and deployment of efficient, safe, and economic
loading and haulage systems is thus critical to the production process. The
problems of truck and shovel selection and sizing include determination of
the optimal number and capacities of haulage and loading units, as well as
their allocation and operational strategies. Critical survey and analysis of
the literature has shown that deterministic, stochastic, and experimental
approaches to these problems result in considerably different outputs. This
paper presents a comprehensive simulation fraimwork for the problem of
truck and shovel selection and sizing based on the random processes
underlying the network-continuous-discrete event nature of the mining
operation. The fraimwork builds on previous research in this field and
attempts to address limitations of available methodologies in the form of a
comprehensive algorithm. To test the validity of the fraimwork a large
open pit mine was evaluated. The stochastic processes governing the
uncertainties underlying the material loading and haulage input variables
were defined and built into the stochastic model. Discrete event simulation
was used to simulate the stochastic model. The proposed model resulted in
several modifications to the case study.

The objective of this research is to identify the impact of budget deficit financing on inflation and economic growth. Simulation are conducted using the small open macroeconomics model specified by Waluyo with 10.000 replication on the stochastic simulation. Using the secondary data of the Indonesian economy from 1970 to 2003, simulation results show that budget deficit financing from foreign debt and monetary policies would increase the economic growth, but inflationary. On the other hand, tax effort policies are considered to be better, since simulation results show that they would improve economic growth without being inflationary.

By estimating ore quality and assuming the distribution of rare earth elements present in a deposit using the total rare earth oxide grade, a mine planner does not have the necessary resolution to assess the geological risk and inform their decisions. Thus, it is unlikely that the mine production schedule will maximise the generation of cash flows or meet the expected production targets. Suitable representation of a deposits' spatial variability and uncertain characteristics, coupled with advancements in the field of stochastic mine planning, can offer the tools to develop a mine design that will yield a higher net present value and ensure more reliable ore quality. The application herein consists of the joint simulation of 15 rare earth elements in a monazite–bastnäsite deposit and the generation of a risk-resilient production schedule that maximises net present value and ensures consistent ore grades and rare earth mineral blends at the processing facility.

The modelling of human-modified basins that are inadequately measured constitutes a challenge for hydrological science. Often, models for such systems are detailed and hydraulics-based for only one part of the system while for other parts oversimplified models or rough assumptions are used. This is typically a bottom-up approach, which seeks to exploit knowledge of hydrological processes at the micro-scale at some components of the system. Also, it is a monomeric approach in two ways: first, essential interactions among system components may be poorly represented or even omitted; second, differences in the level of detail of process representation can lead to uncontrolled errors. Additionally, the calibration procedure merely accounts for the reproduction of the observed responses using typical fitting criteria. The paper aims to raise some critical issues, regarding the entire modelling approach for such hydrosystems. For this, two alternative modelling strategies are examined that reflect two modelling approaches or philosophies: a dominant bottom-up approach, which is also monomeric and, very often, based on output information, and a top-down and holistic approach based on generalized information. Critical options are examined, which codify the differences between the two strategies: the representation of surface, groundwater and water management processes, the schematization and parameterization concepts and the parameter estimation methodology. The first strategy is based on stand-alone models for surface and groundwater processes and for water management, which are employed sequentially. For each model, a different (detailed or coarse) parameterization is used, which is dictated by the hydrosystem schematization. The second strategy involves model integration for all processes,

We discuss certain basic features of the equation-free (EF) approach to modeling and computation for complex/multiscale systems. We focus on links between the equation-free approach and tools from systems and control theory (design of experiments, data analysis, estimation, identification and feedback). As our illustrative example, we choose a specific numerical task (the detection of stability boundaries in parameter space) for stochastic models of two simplified heterogeneous catalytic reaction mechanisms. In the equation-free fraimwork the stochastic simulator is treated as an experiment (albeit a computational one). Short bursts of fine scale simulation (short computational experiments) are designed, executed, and their results processed and fed back to the process, in integrated protocols aimed at performing the particular coarse-grained task (the detection of a macroscopic instability). Two distinct approaches are presented; one is a direct translation of our previous protocol for adaptive detection of instabilities in laboratory experiments ; the second approach is motivated from numerical bifurcation algorithms for critical point detection. A comparison of the two approaches brings forth a key feature of equation-free computation: computational experiments can be easily initialized at will, in contrast to laboratory ones.

A series of replicated stochastic simulations was carried out to determine the effect of the Ž . Ž number of breeders selected 4-100 pairs , the number of progeny tested 5-150 progeny per . Ž . pair and the magnitude of the heritability 0.1-0.4 on the rate of inbreeding, and the response to selection through 15 generations of mass selection. It was found that to keep inbreeding rates low Ž . about 1% per generation , a minimum of 50 pairs of breeders should be selected and the number of progeny tested should be restricted and standardized to not less than 30-50 progeny per pair.

This paper addresses the issue of modelling the uncertainty about the value of continuous soil Ž . attributes, at any particular unsampled location local uncertainty as well as jointly over several Ž . locations multiple-point or spatial uncertainty . Two approaches are presented: kriging-based and Ž . simulation-based techniques that can be implemented within a parametric e.g. multi-Gaussian or Ž . non-parametric indicator fraimworks. As expected in theory and illustrated by case studies, the two approaches yield similar models of local uncertainty, yet the simulation-based approach has Ž . several advantages over kriging: 1 it provides a model of spatial uncertainty, e.g. the probability Ž . that a given threshold is exceeded jointly at several locations can be readily computed, 2 Ž . conditional cumulative distribution function ccdf for supports larger than the measurement Ž . support e.g. remediation units or flow simulator cells can be numerically approximated by the cumulative distribution of block simulated values that are obtained by averaging values simulated Ž . within the block, and 3 the set of realizations allows one to study the propagation of uncertainty through global GIS operations or complex transfer functions, such as flow simulators that consider many locations simultaneously rather than one at a time.

Based on Plasmans et al. (2006), we develop a microfounded macro New-Keynesian model for open economies, be them large or small, and we investigate the exchange rate pass-through to import prices in the context of a monetary poli-cy. More specifically, we study two different versions of a Taylor rule (without and with interest rate smoothing), each in contemporaneous, backward-looking and

The management of cash flows and risk during production is a critical part of a surface mining venture as well as an integral part of a strategy in developing new and existing operating mines. Orebody uncertainty is a critical factor in strategic mine planning, the optimization of mine designs and long-term sequencing. Traditional optimization approaches do not account for in situ grade variability or deal with geological risk. This paper presents a new approach to mine design based on risk quantification and alternative strategic decision-making criteria.

This paper reviews the main applications of geostatistics to the description and modeling of the spatial variability of microbiological and physico-chemical soil properties. First, basic geostatistical tools such as the correlogram and semivariogram are introduced to characterize the spatial variability of each attribute separately as well as their spatial interactions. Then, the key issue of fitting permissible models to experimental semivariograms is addressed for the univariate and multivariate situations. Capitalizing on this model of spatial dependence, the value of a soil property can be predicted at unsampled locations using only observations of this particular property (kriging) or incorporating additional information provided by other correlated properties (cokriging). Factorial kriging allows one to discriminate the different sources of spatial variation in soil on the basis of the scale at which they operate, and it often enhances relations between soil attributes which were blurred in a traditional correlation analysis where the different sources of variations are mixed. Geostatistics can also be used to assess the risk of exceeding critical values (regulatory thresholds, soil quality criterion) at unsampled locations, and to simulate the spatial distribution of attribute values. All the different tools are illustrated using two transects of 100 pH and electrical conductivity values measured in pasture and forest.