Mathematical Modeling of Biofilms, IWA Task Group on Biofilm Modeling. IWA Publishing, Alliance House, 12 Caxton St., London, SWlH OQS, U.K.

This book provides guidelines for the selection and use of mathematical models of biofilms. The whole range of existing models - from simple analytical expressions to complex numerical models - is covered. The application of the models for the solution of typical problems is demonstrated, and the performance of the models is tested in comparative studies. With the dramatic evolution of the computational capacity still going on, modeling tools for research and practice will become more and more significant in the next few years. This report provides the foundation to understand the models and to select the most appropriate one for a given use. Mathematical Modeling of Biofilms gives a state-of-the-art overview that is especially valuable for educating students, new biofilm researchers, and design engineers. Through a series of three benchmark problems, the report demonstrates how to use the different models and indicates when simple or highly complex models are most appropriate.

Occupancy Estimation and Modeling.Darryl I. MacKenzie, James D. Nichols, J. Andrew Royle, Kenneth H. Pollock, Larissa L. Bailey, and James E. Hines. 2006. Elsevier-Academic, San Diego, California, xviii + 324 pp., 23 figures, appendix. ISBN 0-12-088766-5. Hardback, $64.95.-Species presence and absence (i.e., occupancy) data are increasingly being used by avian biologists to assess the status, distribution, and dynamics of bird populations (e.g., Oison et al. 2005, Tornberg et al. 2005, Karanth et al. 2006) and for developing conservation strategies (e.g., Freemark el al. 2006, Jiguet and Julliard 2006). Unfortunately, complete detection of a species is usually impossible, and the ability to detect a species is often related to species-specific traits and the physical characteristics of sample units (reviewed in Thompson 2002). Consequently, incomplete detection can bias occupancy estimates and impede the ability to make sound conservation decisions. Several methods have recently been developed to incorporate incomplete detection in occupancy models (MacKenzie et al. 2002, 2003, 2004; Dorazio and Royle 2005; MacKenzie and Royle 2005). These pioneering efforts, however, have been presented as separate works and often in a manner that was difficult for all but the most technically savvy to understand. This book is an attempt to synthesize existing ideas on occupancy estimation and modeling in a form that is understandable to biologists and ecologiste without strong statistical backgrounds.

The book is a well-organized and comprehensive treatment of occupancy estimation that entails multiple aspects including sample design, analysis, and interpretation. The first three chapters cover basic ecological and statistical background and introduce terminology and concepts that are used throughout the book. Chapter 1 is a philosophical treatment of the nature of science and management and the role of field surveys and monitoring. This philosophy is reflected in much of the material presented throughout the book. We believe that such context is important and often lacking from statistics-oriented texts. Chapter 2 provides an overview of the ecological aspects of occupancy and includes a description of metapopulation dynamics. Although the chapter is not intended to be a thorough review, the authors have done a commendable job compiling and synthesizing an abundance of information on metapopulation dynamics as it relates to occupancy estimation. Chapter 3 is an excellent and thorough review of the basic principles of statistical estimation and inference that should prove useful for professionals and for graduatelevel instruction. The chapter thoroughly details all aspects of parametric statistics: maximum likelihood estimation, hypothesis testing, goodness-of-fit, and model selection. However, none of these topics is covered in relation to Bayesian methods. As such, readers will have no basis for evaluating the goodness-of-fit, convergence, and selection of Bayesian models. Yet later chapters include computer code for fitting Bayesian occupancy models. We believe that this is a potentially hazardous combination and hope that the authors can remedy the problem in future editions. Chapters 4-7 cover single-species occupancy estimation and gradually build from relatively simple, constant detection-probability estimation (chapter 4) to more complex, multiple-season models (chapter 7). Each chapter begins with a useful general introduction and explanation of purpose. Models are then derived in logical sequences, with sufficient mathematical details and clear, concise explanations that should satisfy and enlighten biologists, whatever their level of statistical proficiency. Each chapter contains at least two examples that are used to illustrate model fitting, parameter estimation, and the presentation and interpretation of results. The material in this section of the book is very thorough and is generally presented in a logical sequence. Chapter 6, which covers the design of single-season occupancy studies, includes a thorough evaluation and discussion of factors that are crucial for developing efficient and effective occupancy studies (e.g., study site selection, allocation of sampling effort). However, the chapter is probably of limited use for developing monitoring designs. Chapter 7 (multiple-season models) provides useful study-design guidance that is relevant to monitoring (e.g., the limitations of a rotating panel design), but lacks detail on statistical power. A general treatment of study design that included the details in both chapters 6 and 7 would have been preferable.

Chapters 8 and 9 deal with two ways to investigate multiple-species occupancy patterns: (1) interactions among a small number of species (chapter 8) and (2) changes in species richness (chapter 9). As the authors acknowledge in the introduction to chapter 8, these two chapters are not as well developed as previous sections of the book, providing few, if any, examples for each method. The lack of associated software and example code in this section, with the exception of the two-species interaction model implemented in PRESENCE, version 2 (Hines 2006), will limit the use of these methods to statistically savvy readers with knowledge of computer programming. Despite the lack of implementation detail and the paucity of examples, the authors do an excellent job, as in previous sections, in presenting the material in a logical order and clearly deriving and explaining all models in a way accessible to all biologists. Analysis of occupancy data at the community level is also a very active area of research, and we expect user-friendly software implementing many of the methods described here to become available in the near future.

The goal of this research was to develop a methodology for modeling a bioinfiltration best management practice (BMP) built in a dormitory area on the campus of Villanova University in Pennsylvania. The objectives were to quantify the behavior of the BMP through the different seasons and rainfall events; better understand the physical processes governing the system's behavior; and develop design criteria. The BMP was constructed in 2001 by excavating within an existing traffic island, backfilling with a sand/soil mixture, and planting with salt tolerant grasses and shrubs native to the Atlantic shore. It receives runoff from the asphalt (0.26 hectare) and turf (0.27 hectare) surfaces of the watershed. Monitoring supported by the hydrologic model shows that the facility infiltrates a significant fraction of the annual precipitation, substantially reducing the delivery of nonpoint source pollution and erosive surges downstream. A hydrologic model was developed using HECHMS to represent the site and the BMP using Green-Ampt and kinematic wave methods. Instruments allow comparison of the modeled and measured water budget parameters. The model, incorporating seasonally variable parameters, predicts the volumes infiltrated and bypassed by the BMP, confirming the applicability of the selected methods for the analysis of bioinfiltration BMPs.The urbanization of a watershed, with the associated increase in impervious surface and intensity of use, changes the local hydrology and that of the downstream river system. Paved areas and roofs, as well as compacted turf areas, speed increased runoff volumes to the receiving channels with diminished opportunity for filtration of pollutants. Increased flows downstream cause more frequent flooding as well as accelerated stream channel erosion. Meanwhile, these pulses of excess runoff are not contributing to ground water recharge, leading to lower stream base flows and urban water supply problems during dry periods. Additionally, the pavement, buildings, and turf areas significantly reduce the opportunity for evapotranspiration and the environmental benefits it confers (Schueler, 1995; USEPA, 2002).

To compensate, many BMPs have been developed since the link between increasing impervious areas and watershed scale problems has been recognized. In the past, stormwater management techniques (especially detention basins) emphasized flood control without considering the corollary impacts of development on increasing pollution and decreasing ground water recharge. The long term effects of some of these attempts have now been studied and quantified (Emerson, 2000; Roesner and Nehrke, 2004). The accumulated experience has given rise to new solutions and new governmental regulations. For example, the Pennsylvania Comprehensive Stormwater Policy, announced in 2003, states that

"planners and applicants should evaluate and utilize infiltration BMPs to manage the net change in stormwater generated or otherwise replicate to the maximum extent possible preconstruction stormwater infiltration and runoff conditions so that post construction stormwater discharges do not degrade the physical, chemical, or biological characteristics of the receiving waters" (PaDEP, 2002).

As the above described movement in the state-ofthe-art of stormwater management becomes a part of planning requirements, of engineering practice, and of regulatory review, it becomes necessary to quantify the effects of infiltration BMPs. The designer must be able to demonstrate with confidence that a proposed BMP will behave in a predictable and beneficial way one that suits the needs of the developers, the architects and planners, the reviewing officials, the future residents, and those downstream. Current regulations and design guidelines are still being developed, and some resemble "rules of thumb" rather than specific and objective criteria.

The objectives of this modeling study are to contribute to the acceptance and successful application of bioinfiltration BMPs. By basing a modeling approach on the topography of the site, the geometry of the BMP, and the physical characteristics of the cover and soils, the capability for analyzing the BMP's performance is established. By explicitly considering the physical properties of the site, the modeling approach provides a way to cross-check initial site observations or design assumptions with measurable performance and to make adjustments without resorting to the use of "correction factors" that may have no directly observable reference. And by explicitly considering the different temporal patterns of storms and the variability of the response of the watershed and the BMP over the course of the seasons, the modeling approach provides a more realistic way to develop descriptive statistics. A successful physically based model provides a logical way to move from general performance measures and site observations to the specifics for the design of a BMP.

A Systems Analysis and Design course should develop both the technical as well as interpersonal skills of each student. Each student must be able to develop and use the various lifecycle models and be able to communicate with end users through these models. By creating interleaved modeling and writing assignments within the Systems Analysis and Design course both objectives can be met. This paper presents a series of integrated modeling and writing assignments-used in a Systems Analysis and Design course-that have been developed to enhance both the technical and interpersonal skills of an IS student.

The Systems Analysis and Design course within Information Systems curriculum provides the student with the skills necessary to analyze and design information systems (Gorgone, Davis et al. 2003). One of the major objectives of this course is to have the student develop and use each of the models-either structured or OO-in the Systems Development Life Cycle (SDLC) (Hasan 2002). A second objective is to make each student aware of the interpersonal skills necessary for successful systems development (Guinan and Bostrom 1986; Gorgone, Davis et al. 2003). In particular, the Systems Analysis and Design course should emphasize "the factors for effective communication and integration with users" (Gorgone, Davis et al. 2003, pg. 29). In fact, the models developed in the SDLC are rendered useless unless "effective communication patterns are used by developers and users" (Guinan and Bostrom 1986, pg. 3).

These two objectives-model development and interpersonal/communication skills-are met simultaneously through series of assignments developed for a Systems Analysis and Design course. In the course, the student is required-individually and then as part of a group-to develop a series of SDLC models and write a corresponding memo that explains the purpose, use, and their understanding of each model. This article describes how these assignments are used to meet these two learning objectives simultaneously.

2. SYSTEMS ANALYSIS AND DESIGN COURSE AND ASSIGNMENTS

In a typical Systems Analysis and Design course, topics range from planning to design and development activities, including the implementation of a database or other information system. However, this course is taught over a seven week period so only the activities within the planning, analysis, and design stages are addressed. The focus of the course is on the first objective-the development and use of the models in the structured approach-however, the course is regarded as a writing intensive course by the University; therefore a significant writing component must be incorporated into the course (Pomykalski 2005). Since this course, like nearly 75% of other Systems Analysis and Design courses, focuses on the structured approach, it should be useful to many instructors (Mahapatra, et al., 2005).

Each student is given a series of four models to develop throughout the course. The models are for economic feasibility (return on investment, breakeven analysis, and net present value), data modeling (an entity-relationship diagram), process modeling (a dataflow diagram), and database design (a database schema). As part of each modeling assignment, the student creates a two page memo that explains the purpose, use, and specifics of the corresponding model in their own words. These individual modeling and memo-writing assignments are done using a straightforward case adapted from a textbook (Satzinger, Jackson et al. 2004). Each assignment is then graded and returned to the student.

In order to assess the learning from the initial assignment, the same assignment, using a more complex case study (similar to ABC Church (Cappel 2001)), is completed by student groups; typically 2-3 students per group. Each student group develops the model, and writes a corresponding memo, for economic feasibility, data modeling, process modeling, and database design. In this way, concept learning is assessed.

Designed for any physics-based system, COMSOL Multiphysics v3.3[R] offers simulation and virtual prototyping for various fields of science, research, and engineering. Features include Model Tree that gives overview of all aspects of model, interactive meshing, merging components to build models, ability to handle CAD assemblies, and support for multiphysics analysis of surface contact. Swept meshing tool allows users to create prism (wedge) meshes and hexahedral (brick) meshes.

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BURLINGTON, MA (September 22, 2006) - With its enhanced features, COMSOL Multiphysics 3.3[R] brings simulation and virtual prototyping to a far wider community of engineers. The release also greatly expands the number of possible application areas for the technology into virtually every field of science, research, and engineering. Key among these enabling features are ready-made couplings between common physics, an even more convenient user interface thanks to a Model Tree, interactive meshing, merging components to build models, the ability to handle CAD assemblies, support for the multiphysics analyis of surface contact, and gains towards fully automatic solver selection. Users can also expand on the package's internal material database by enacting an online search of the Matweb[R] database and directly importing material properties.

An aspect that makes it easier for the average engineer to successfully create models is the increased number of predefined multiphysics couplings. Users know intuitively what they want to do, for example, evaluate fluid-structure interaction. Until now, though, to implement unusual or extremely complex models they have needed a fairly thorough knowledge of the underlying physics along with familiarity of COMSOL Multiphysics' dialog boxes and the overall problem structure. Now they simply select ready-made couplings from a menu with the correct physics, boundary settings, and couplings already set up. Users then quickly modify this interface to meet the specific needs of the geometry

A number of new predefined multiphysics couplings join an already comprehensive selection. These include microwave heating, induction heating, rotating machinery, fluid-thermal interactions for laminar, nonisothermal and turbulent flow, and fluid-structure interaction.

A major addition to the COMSOL Multiphysics graphical user interface is the Model Tree. This separate window gives an overview of all aspects of a model by means of a menu-tree view that users can navigate to inspect and modify context-specific features and settings. All variables, parameters, constants, and expressions are accessible from the Model Tree. A special condensed view shows only where a user has made modifications that deviate from default settings.

Solving made straightforward, more transparent

When a model is set up, the software now takes a first step towards fully automating the solver choice, a choice that is fully aware of the mathematics and numerical schemes required to solve the multiphysics couplings. The version also adds the PARDISO solver-a shared-memory parallel algorithm that works well as a powerful direct solver applicable to, for example, large electromagnetic models.

During the solution process a realtime probe-plot feature tracks the value of any selected variable and graphs this scalar value in real time. Similarly, while the software is calculating the solution, users can monitor a convergence plot that shows the solver's progress on a realtime graph.

Parts and assemblies

Most CAD engines typically work with parts and assemblies, and COMSOL Multiphysics now supports them throughout the modeling process. Rather than import an assembly as a single unit, COMSOL Multiphysics now recognizes its constituent components, each with multiple parts, for instance to allow for different materials in each one. Parts and their physics can be coupled through a feature that allows for continuity or allows users to define other internal border definitions such as contact resistance.

Users can also start working with a library of components where each contains not only a geometry but also a specific physics definition, boundary settings, and set of material properties. Two or more components are then merged to build a more comprehensive system, process, or assembly, all without each component's settings being lost. This is ideal for a user investigating many different models that vary only in a certain part or section of the overall makeup.

Interactive meshing

It is possible to optimize the mesh locally for each part or model subdomain through the interactive meshing environment. This makes it possible to build a mesh in an incremental fashion where each meshing operation acts on a set of subdomains. For example, users can start by creating a boundary mesh and then mesh each subdomain sequentially. Furthermore, using interactive meshing they can apply different meshing techniques to different domains of a geometry object. Outside of the obvious benefits for matching a mesh to a subdomain's geometry, this feature also provides improved flexibility as sometimes the mesh must suit the physics found within one subdomain that may not exist in other subdomains. The interactive meshing feature does not require a nodal match on the boundaries between the different subdomains, instead connection occurs through the mathematics of the numerical scheme.

Recent water sector reforms and increased scarcity and vulnerability of water resources, combined with declining public funding available for large scale infrastructure investment in the sector, have led to a greater awareness by the Government of Vietnam for the need to analyze water resource allocation and use in an integrated fashion, at the basin scale, and from a perspective of economic efficiency. In this study we focus on the development, application, and selected policy analyses using an integrated economic hydrologic river basin model for the Dong Nai River Basin in southern Vietnam. The model framework depicts the sectoral structure and location of water users (agriculture, industry, hydropower, domestic, and the environment) and the institutions for water allocation in the basin. Water benefit functions are developed for the major water uses subject to physical limitations and to constraints of system control and policy. Based on this modeling framework, we will analyze policies that can affect water allocation and use at the basin level, including both basin-specific and general macroeconomic policies.

Fresh water, essential to sustain life, development, and the environment, is an increasingly vulnerable resource in Asia, where population and economic growth creates serious challenges for meeting water demands for food requirements and other uses. Asian countries face rising competition for water resources from rural and urban sectors for agricultural, industrial, and household uses, combined with declining investments in infrastructure. Water quality is increasingly threatened as agricultural, industrial (including rural industrial), and domestic users in increasingly crowded watersheds compete for scarce water supplies. In this water scarce and often polluted environment, it is essential to understand the impacts of specific policy alternatives for water allocation among these users: whether they are feasible, what they will cost, and how they will affect water users and the overall prospects for agricultural production and economic growth.

Water policy analysis requires the application of comprehensive policy analysis tools that need to be adapted and expanded depending on the river basin in question. Economic optimization models that optimally allocate water based on an objective function and accompanying constraints, along with hydrologie simulation models and a representation of institutional rules, can be complementary tools to traditional river basin simulation models to address problems related to the competition over scarce water resources and the design and assessment of alternative systems of water allocation. In this study we take a holistic approach in analyzing the complexities involved in water allocation to generate options for water policy reform in the Dong Nai River Basin for optimal utilization that is also sustainable, efficient, and equitable (Rogers and Fiering, 1986; McKinney et al., 1999). The alternative policy analyses presented here have been developed with and can support the recently established Dong Nai River Basin Planning Management Council. The policy scenarios developed can help policy makers and basin water planners gain a better understanding of water availability, demand, and its value in various uses; provide insights into the role of alternative policy instruments in alleviating likely future water shortages; and thus contribute to more efficient and sustainable allocation of scarce water and financial resources across irrigation, hydropower development, and urban demands.

Natural channel designs often incorporate rigid instream structures to protect channel banks, provide grade control, promote flow deflection, or otherwise improve channel stability. The long term impact of rigid structures on natural stream processes is relatively unknown. The objective of this study was to use long term alluvial channel modeling to evaluate the effect of rigid structures on channel processes and assess current and future stream channel stability. The study was conducted on Oliver Run, a small stream in Pennsylvania relocated due to highway construction. Field data were collected for one year along the 107 m reach to characterize the stream and provide model input, calibration, and verification data. FLUVIAL-12 was used to evaluate the long term impacts of rigid structures on natural channel adjustment, overall channel stability, and changing form and processes. Based on a consideration of model limitations and results, it was concluded that the presence of rigid structures reduced channel width-to-depth ratios, minimized bed elevation changes due to long term aggradation and degradation, limited lateral channel migration, and increased the mean bed material particle size throughout the reach. Results also showed how alluvial channel modeling can be used to improve the stream restoration design effort.

Autodesk Maya v8.5 features Maya Nucleus, which allows creation of elements that interact in 3D with objects such as fluids, cloth, and rigid bodies. Maya nCloth allows direction and control of simulations including cloth, plastic, and metal, while air-pressure model enables use of any geometry from closed, sealed volumes to open volumes such as balloons. Utilizing python scripting, software is available for Mac, Windows, and Linux platforms.

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SAN RAFAEL, Calif., Jan. 15 // -- Autodesk, Inc. (NASDAQ:ADSK) today announced Autodesk Maya 8.5, the latest version of its Maya 3D animation, modeling and rendering software. Maya is widely used for games development, as well as film and television visual effects production. Now shipping, Maya 8.5 gives artists enhanced creative control, enabling faster completion of complex animations and simulations.

"Autodesk is committed to making Maya the foundation for modern production pipelines. Maya 8.5 supports industry-standard Python scripting, offering improved workflows and development productivity," said Marc Petit, Autodesk's Media & Entertainment vice president. "We're excited to offer Maya 8.5 as a Universal application for both Intel-based and PowerPC-based Macintosh computers. As well, the software features innovative new capabilities for character animation; the new Maya Nucleus unified simulation framework enables interactive simulations while keeping artists in full control of the animation."

Maya 8.5 includes Maya nCloth, which is the first module of Maya Nucleus. With Maya nCloth, artists can quickly direct and control a range of simulations, including cloth, plastic, metal and other materials. Believable cloth-on-cloth simulations with complex cloth collisions, such as a cape over a jacket, can be created more easily. A unique air-pressure model enables artists to use any geometry -- whether a closed, sealed volume such as an inner tube, or an open volume such as a balloon -- to create an inflatable object with internal and external pressure.

Python scripting is also new in Maya 8.5. This popular open-source programming language helps accelerate facility-specific custom script development and plug-in prototyping, extending and automating Maya production pipelines. Python scripting offers a powerful alternative to Maya software's native scripting language, MEL, while featuring the same deep level of integration with the Maya command engine. Python scripting further augments the creative control gained with Maya Nucleus, giving scriptwriters the ability to efficiently manipulate, customize and automate the software.

Anders Langlands, R&D lead at The Moving Picture Company (MPC) and Maya 8.5 beta tester, commented: "Having Python support available in Autodesk Maya means we can leverage many of our existing tools directly within Maya, rather than having to write glue code to bind Maya to our pipeline. This allows us to develop new node and command plug-ins in a fraction of the time it would normally take using other solutions." London, UK-based MPC offers visual effects and post-production services for advertising, television and feature film.

Maya 8.5 is available as a Universal application release for Intel- and PowerPC-based Macintosh computers (announced separately), as well as on the Microsoft Windows and Linux platforms. The software includes a number of new artist-driven features and performance optimizations. For a complete list of features in Maya 8.5, please visit www.autodesk.com/maya.

*In 2006, Autodesk Principal Research Scientist Jos Stam was honored with a Technical Achievement Award (Academy certificate) by the Academy of Motion Picture Arts and Sciences. The award acknowledges Stam's research on subdivision surfaces and contributions to the motion picture industry.

About Autodesk

Autodesk, Inc. is a Fortune 1000 company, wholly focused on ensuring that great ideas are turned into reality. With seven million users, Autodesk is the world's leading software and services company for the manufacturing, building, infrastructure, wireless data services and media and entertainment fields. Autodesk's solutions help customers create, manage and share their data and digital assets more effectively. As a result, customers turn ideas into competitive advantage by becoming more productive, streamlining project efficiency and maximizing profits.

Demands for improved oil recovery prompted the Norwegian oil company, Statoil, to evaluate geologically complex oil and gas-condensate fields offshore Norway with a new approach. Here, major sections of producing reservoirs are heterolithic tidal units of interlayered mud and sand.

Using conventional modeling technology, Statoil geologists could not capture the fine-scale interlayering that would later impact their reservoir property simulations and reserve predictions. A unique multi-scale reservoir modeling tool developed by Geomodeling enabled Statoil to better understand its reservoir assets and choose the right strategies for optimized recovery.

Compared to homogeneous reservoirs with few structural and sedimentary complexities, heterogeneous reservoirs have relatively low recovery factors, Fig. 2. This is due to the effects of compartmentalization, multi-phase flow and pressure development within the reservoir. These effects complicate reservoir predictions, drainage strategies and improved oil recovery (IOR) measures.

[FIGURE 2 OMITTED]

Statoil geologists recognized that the key to improving reservoir predictions was to model the observed heterogeneities in detail and evaluate their collective effect on flow properties. The reservoir properties that affect fluid flow and distribution--such as porosity, permeability and mud/sand volumes--are governed by small-scale (cm-to-m scale) geometries occurring below conventional well log or seismic resolution. For example, grain size contrasts between mm-to-cm scale sand and mud lamina set up strong permeability anisotropy. This forces fluids to move along pathways controlled by bedding structures and mud/sand ratios.

A NEW APPROACH

Today's demands for faster reservoir cycle times, coupled with limited CPU capacities, have made it impractical or unfeasible to model small-scale bedforms with conventional techniques. Using SBED proprietary geological modeling software, Statoil successfully modeled the observed heterogeneities in Halten Terrace tidal units. These detailed models were then used to generate effective porosity and permeability values for input to large-scale reservoir simulations and reserve estimations. Results represent the real distribution of porosity and permeability in reservoir intervals, and provide more accurate reserve calculations and production profiles.

Process-oriented modeling. The new modeling method follows a process-oriented approach. Process-oriented modeling of sedimentary structures mimics the products of sedimentary processes, such as bedform migration, erosion and deposition, without actually calculating the physics of grain movement or fluid flow. The method combines a vector-based movement of geometric surfaces through space and time with Gaussian simulation to create stochastic models of sedimentary structures and their associated petrophysical properties. (2,3) This approach considers the stochastic nature of sedimentation and the spatial distribution of reservoir properties. (4) The resulting models are highly realistic, Fig. 3.

[FIGURE 3 OMITTED]

Upscaling. Upscaling is the process of extrapolating fine-scale reservoir data (e.g., cm-scale core plug and m-scale well log data) to coarser scales, to populate reservoir grid cells up to hundreds of meters in size. Conventional modeling approaches can upscale the data statistically (e.g., by arithmetic, geometric or harmonic averaging) without considering the effects of fine-scale heterogeneities or data bias. When the upscaled data and associated errors are used to populate large-scale reservoir grids, the resulting reservoir simulations have a high degree of uncertainty.

In the modeling workflow, sedimentary environments, such as fluvial, shoreface or deepwater facies, can be reproduced by creating stacked bedding models from over 100 bedding templates. These small-scale models are populated with petrophysical data (such as porosity and permeability) derived from core plug and well log measurements.

The petrophysical models are upscaled by a range of methods, including averaging and flow simulation, to obtain effective properties for a given bedding structure. The resulting models incorporate the effects of sedimentary structure on flow properties. (5) This approach enables upscaling without overloading the models with data or increasing model sizes. By comparing the effective property relationships derived from multiple SBED scenarios, a user can better discriminate between reservoir and nonreservoir intervals.

Enterprises must continually adapt to changes that occur due to business, political, or technological challenges. These on demand businesses require integration of people, information, and processes in order to conduct business in real time. Meeting the requirements of such a dynamic environment requires leveraging business-to-business (B2B) partnerships and outsourced services by enabling enhanced integration between business processes. For example, supply chain integration of manufacturers and distributors requires deeper examination of sales forecasts, production scheduling, product configuration, and inventory management.

Recently, government requirements for accountability of business practices and information management have transformed security concerns from an isolated piece of the information technology (IT) puzzle into an important and far-reaching business issue that must be addressed. It is no longer sufficient to delegate responsibility to the IT organization alone. Doing so may lead to fragmented business and IT plans along with misallocation and inefficient use of already scarce technology resources.

To satisfy the new demands of a changing marketplace, the industry must adopt a fundamental change in the way application and system integration is accomplished. This change requires an infrastructure that supports loose coupling of intra- and inter-enterprise information among widely disparate application designs, operating systems, databases, and application programming interfaces (APIs). In order to efficiently integrate the varied set of applications and platforms that make up the information technology (IT) infrastructure of these enterprises, the enterprises are beginning to realize the value of a service-oriented architecture (SOA) and to refactor their applications into loosely coupled services. For an enterprise to be a secure on demand business, the enterprise infrastructure must be flexible and customizable to reflect new requirements and regulations. To provide such flexibility, the enterprise should not hardwire (permanently fix) its policies into the infrastructure, but instead allow the security model of the enterprise to be implemented through a policy-driven infrastructure. This is no simple task.

Software, environments, and tools

TK5105

Written for advanced computer science or applied mathematics undergrads and for graduate students in the information sciences who specialize in retrieval systems, this slim volume describes the applied mathematics used in information management as these come together in search engines. Chapters on vector space modeling and link-structure algorithms (the latter is new to this edition) are among the computational methods described. The bibliography has been expanded for this edition. Berry and Browne (both are with the Computer Science department at the U. of Tennessee) developed this text from a successful undergraduate course there.

MF: All of our readers are dying to know: How'd you earn the name "the Pleasure Machine"?

GR: [Laughs] It actually has nothing to do with what most people would think. I did a commercial for Virgin Airways where I played a stripper, in a real strip club, wearing black boots and a black bikini. In the commercial, there was a famous poet doing a voice-over, and he read the words, "Step aboard the pleasure machine," just as they showed me dancing and spinning around the pole. In Australia, everyone has a nickname, so when people saw the commercial, it just stuck. I love it; I think it's great!

What would a guy need to do to earn the label "Pleasure Machine" in your eyes?

He'd have to have a great body! [Laughs] Especially nice, muscled arms or a nice butt. When I'm in the gym and American football is on, I can't resist stating at the players' tight, shiny pants, because most of the guys have such great buns, legs, and thighs.

It's really about chemistry, personality, and making me laugh. If a guy makes me laugh he's halfway there. [Laughs] Combine that with ambition, and you're there.

What was your experiences like on Australia's Celebrity Survivor? Did you anything crazy, like taking your clothes off for chocolate and peanut butter the way Jenna and Heidi did on the American Survivor?

Ha ha, that's funny! No, I didn't. I did run around a lot in my bikini, which was fun. There was a twist in the game--I was the only female on the boys' team, and they ended up doing everything. I just lay on the beach and sunbathed. [Laughs]

We hear you also trained as a dancer for the Australian version of Dancing With the Stars. Is it true what they say, that dancers--what with their flexibility and all--really make some of the best lovers?

I would imagine so.... [Laughs] Yes, I think the fitter you are, the better your sex life is going to be. Definitely!