Level Of Model Complexity Research Articles

Assessing the Perception of Human-Like Mechanical Impedance for Robotic Systems

As physical interactions between robots and humans become more common, there is a growing need to design robots that are kinesthetically perceived as human-like. One approach to implement human-like mechanical impedance is to physically simulate models of the human neuromuscular system. However, the level of model complexity needed to achieve perception of human-like properties is unknown. The purpose of this study was to develop an objective assessment of a human subject's ability to discriminate kinesthetically between a model-defined impedance and that produced by human muscle. The assessment was based upon signal detection theory, by which the ability to discriminate between two classes of stimuli is analyzed statistically. With this assessment, we tested the hypothesis that a nonlinear muscle model is necessary to obtain perception of human-like muscle mechanical impedance. Fifteen subjects were presented with a mechanical impedance of either a simulated muscle model or electrically stimulated wrist muscle and were asked to decide if they were interacting with a “machine” or “human.” The impedances were randomized for a total of 30 presentations. The results showed that a robot that stimulates either linear viscoelastic properties or a nonlinear Hill muscle model could be distinguished from a human wrist muscle by almost all subjects. However, the subjects' ability to discriminate between the Hill model and human muscle was significantly less, which may have been due to larger overall impedance of the viscoelastic model. These results are important for the design of robots that emulates mechanical impedances that are perceived as human-like.

Sound transfer path analysis to model the vibroacoustic behavior of a commercial earmuff

Hearing protection devices (HPD), such as earmuffs, are widely used to protect workers from noisy environments. Numerical predictive tools can be used to simulate the vibroacoustic behavior of earmuffs and thus assess their sound attenuation and improve their acoustical design. The present work describes the implementation of a vibroacoustic finite element numerical model of an earmuff coupled to a rigid baffle in the frequency range from 20 to 5000 Hz. An experimental assessment of the sound transfer paths through each element of the earmuff (cup, cushion, and foam lining) using a specific acoustical test bench is first proposed. This analysis is then used to target the right level of model complexity for each component. An experimental validation of the FEM model is then carried out.

Meso-scale modelling of directional solidification and comparison with in situ X-ray radiographic observations made during the MASER-12 XRMON microgravity experiment

Computational modelling of advanced solidification processes has made considerable advances over the last half century, with ever increasing levels of modelling complexity. There is, therefore, an increasing need for state of the art experimental investigation to provide suitable validation for these model predictions. In situ X-ray radiography has become a powerful tool for solidification experimentation. Using either synchrotron or microfocus X-ray sources, thin samples, encased in X-ray transparent crucibles, can be directionally or isothermally solidified, allowing for direct real time observation of dynamic solidification phenomena. This paper presents the results of a meso-scale Front Tracking simulation of a directional solidification experiment, performed using an Al–20wt.% Cu alloy, carried out under microgravity conditions on board the MASER 12 sounding rocket. The sample was mounted in a Bridgman type gradient furnace and solidified using a prescribed cooling regime with a constant gradient, thus promoting directional solidification in the field of view. The actual thermal gradient in the sample was found to be lower than the nominal thermal gradient, as set/recorded by thermocouples embedded in the heater elements. The adjusted thermal data were supplied as inputs to the Front Tracking model and good agreement was then observed between the model predictions and the in situ observations. The extent and amplitude of the undercooled zone ahead of the columnar front was predicted based on analytical growth kinetics laws and the results were also compared to analytical models of columnar-to-equiaxed transition (CET) prediction.

Testing Paradigms of Ecosystem Change under Climate Warming in Antarctica

Antarctic marine ecosystems have undergone significant changes as a result of human activities in the past and are now responding in varied and often complicated ways to climate change impacts. Recent years have seen the emergence of large-scale mechanistic explanations–or “paradigms of change”–that attempt to synthesize our understanding of past and current changes. In many cases, these paradigms are based on observations that are spatially and temporally patchy. The West Antarctic Peninsula (WAP), one of Earth’s most rapidly changing regions, has been an area of particular research focus. A recently proposed mechanistic explanation for observed changes in the WAP region relates changes in penguin populations to variability in krill biomass and regional warming. While this scheme is attractive for its simplicity and chronology, it may not account for complex spatio-temporal processes that drive ecosystem dynamics in the region. It might also be difficult to apply to other Antarctic regions that are experiencing some, though not all, of the changes documented for the WAP. We use qualitative network models of differing levels of complexity to test paradigms of change for the WAP ecosystem. Importantly, our approach captures the emergent effects of feedback processes in complex ecological networks and provides a means to identify and incorporate uncertain linkages between network elements. Our findings highlight key areas of uncertainty in the drivers of documented trends, and suggest that a greater level of model complexity is needed in devising explanations for ecosystem change in the Southern Ocean. We suggest that our network approach to evaluating a recent and widely cited paradigm of change for the Antarctic region could be broadly applied in hypothesis testing for other regions and research fields.

Monitoring and modelling to support wastewater system management in developing mega-cities

Urban drainage system models can be useful to assess and manage system performance and to plan its development. However, due to data and computational costs, sophisticated, high-resolution contemporary models of the sewer system may not be applicable. This constraint is particularly marked in developing country mega-cities where catchments can be large, data tend to be scarce, and there are many unknowns, for example regarding sources, losses and wrong connections. This paper presents work undertaken over the last 7years to develop a suitable monitoring and modelling framework to support operation and development of the wastewater system of Bogotá (Colombia). Components of the framework covered here are: (a) the flow and water quality database, (b) a wastewater pollution load generator, and (c) a semi-distributed sewer network model, which aims at a complexity that matches the information available from the previous two components. Results from a catchment within Bogotá, area 150km2 and with 2.5million inhabitants, show that the model outputs capture the scale and dynamics of the observed concentrations and loads at various points on the sewer system. However uncertainty is high because much of variability of observed dry weather flow profiles is apparently random. Against this variability, the effects of in-sewer processes were not identifiable except where backwaters caused particularly high retention times. Hence the work has resulted in an operational model with a scientifically justified, yet useful, level of complexity for Bogotá. More generally, the work demonstrates the value of monitoring and modelling programmes, including having modellers actively involved in monitoring specification and operations; and the insights into suitable level of model complexity that may be gained by uncertainty and sensitivity analysis.

Evaluating the validity of spectral calibration models for quantitative analysis following signal preprocessing

When paired with high-powered chemometric analysis, spectrometric methods offer great promise for the high-throughput analysis of complex systems. Effective classification or quantification often relies on signal preprocessing to reduce spectral interference and optimize the apparent performance of a calibration model. However, less frequently addressed by systematic research is the affect of preprocessing on the statistical accuracy of a calibration result. The present work demonstrates the effectiveness of two criteria for validating the performance of signal preprocessing in multivariate models in the important dimensions of bias and precision. To assess the extent of bias, we explore the applicability of the elliptic joint confidence region (EJCR) test and devise a new means to evaluate precision by a bias-corrected root mean square error of prediction. We show how these criteria can effectively gauge the success of signal pretreatments in suppressing spectral interference while providing a straightforward means to determine the optimal level of model complexity. This methodology offers a graphical diagnostic by which to visualize the consequences of pretreatment on complex multivariate models, enabling optimization with greater confidence. To demonstrate the application of the EJCR criterion in this context, we evaluate the validity of representative calibration models using standard pretreatment strategies on three spectral data sets. The results indicate that the proposed methodology facilitates the reliable optimization of a well-validated calibration model, thus improving the capability of spectrophotometric analysis.

Modelling dynamics in decision support systems

Decision support systems (DSS) play an important role in supporting scheduling tasks in industrial settings. In this article, a special aspect of DSS for scheduling, namely their development, implementation and usage in relation to user participation, is examined. The outlined research model proposes that different forms of user participation lead to different levels of model complexity. This complexity influences in turn such outcome variables like system performance and user satisfaction. The research model integrates several existing concepts from technology acceptance, planned behaviour, involvement and participation research as well as control theory. These theories are expanded by the dynamics of the modelling process. For the empirical analysis, multiple case studies examining different cases of DSS development and implementation processes in different settings were conducted.

Developing and applying heterogeneous phylogenetic models with XRate.

Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the “grammar” of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate 's new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate 's flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART.

An activated sludge modeling framework for xenobiotic trace chemicals (ASM‐X): Assessment of diclofenac and carbamazepine

Conventional models for predicting the fate of xenobiotic organic trace chemicals, identified, and calibrated using data obtained in batch experiments spiked with reference substances, can be limited in predicting xenobiotic removal in wastewater treatment plants (WWTPs). At stake is the level of model complexity required to adequately describe a general theory of xenobiotic removal in WWTPs. In this article, we assess the factors that influence the removal of diclofenac and carbamazepine in activated sludge, and evaluate the complexity required for the model to effectively predict their removal. The results are generalized to previously published cases. Batch experimental results, obtained under anoxic and aerobic conditions, were used to identify extensions to, and to estimate parameter values of the activated sludge modeling framework for Xenobiotic trace chemicals (ASM-X). Measurement and simulation results obtained in the batch experiments, spiked with the diclofenac and carbamazepine content of preclarified municipal wastewater shows comparably high biotransformation rates in the presence of growth substrates. Forward dynamic simulations were performed using full-scale data obtained from Bekkelaget WWTP (Oslo, Norway) to evaluate the model and to estimate the level of re-transformable xenobiotics present in the influent. The results obtained in this study demonstrate that xenobiotic loading conditions can significantly influence the removal capacity of WWTPs. We show that the trace chemical retransformation in upstream sewer pipes can introduce considerable error in assessing the removal efficiency of a WWTP, based only on parent compound concentration measurements. The combination of our data with those from the literature shows that solids retention time (SRT) can enhance the biotransformation of diclofenac, which was not the case for carbamazepine. Model approximation of the xenobiotic concentration, detected in the solid phase, suggest that between approximately 1% and 16% of the total solid carbamazepine and diclofenac concentrations, respectively, is due to sorption-the remainder being non-bioavailable and sequestered. We demonstrate the effectiveness of the model's predictive power over conventional tools in a statistical analysis, performed at four levels of structural complexity. To assess WWTP retrofitting needs to remove xenobiotic trace chemicals, we suggest using mechanistic models, e.g., ASM-X, in regional risk assessments. For preliminary evaluations, we present operating charts that can be used to estimate average xenobiotic removal rates in WWTPs as a function of SRT and the xenobiotics mass loads normalised to design treatment capacity.

Evaluating a suitable level of model complexity for finite element analysis of the intact acetabulum

To enable large-scale multi-factorial finite element (FE) studies, the FE models used must be as computationally efficient as is feasible, while maintaining a suitable level of definition. The present study seeks to find an optimum level of model complexity for use in such large-scale studies by investigating which model attributes are most influential over the chosen model outputs of principal stress and strain in the intact acetabulum. A multi-factorial sensitivity study was carried out using 128 FE models, representing combinations of the following variables: bone stiffness distribution, imposed muscle loading, boundary condition location, hip joint contact conditions and patient's bone anatomy. The relative sensitivity of each input factor was analysed, and it was concluded that the optimum level of model definition must include CT-dependent trabecular bone properties and a sliding interface at the hip joint. It was found that it was not essential to describe the ligamentous sacroiliac and pubic symphysis joints; these could be rigidly fixed in space; and for the normal walking load case, muscle forces may be neglected. It was also concluded that a variety of bone anatomies should be included in a multi-factorial analysis if results are to be inferred for a wider population.

Precisely wrong or roughly right? An evaluation of development viability appraisal modelling

PurposeThe purpose of this paper is to investigate the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs.Design/methodology/approachMonte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance.FindingsIt is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, that simple and simplistic models may produce similar outputs to more robust and disaggregated models. Evidence is found of equifinality in the outputs of a simple, aggregated model of development viability relative to more complex, disaggregated models.Originality/valueDevelopment viability appraisal has become increasingly important in the planning system. Consequently, the theory, application and outputs from development appraisal are under intense scrutiny from a wide range of users. However, there has been very little published evaluation of viability models. This paper contributes to the limited literature in this area.

An Assessment of State-and-Transition Models: Perceptions Following Two Decades of Development and Implementation

State-and-transition models (STMs) are being developed for many areas in the United States and represent an important tool for assessing and managing public and private rangelands. Substantial resources have been invested in model development, yet minimal efforts have been made to evaluate the utility of STMs for rangeland assessment and management. We interviewed 47 rangeland professionals, equally divided between managers and researchers, in four ecoregions to determine their perceptions of the purpose, development, and strengths and weaknesses of STMs to assess the status of the STM framework. Our analysis identified three primary perspectives regarding the purpose of STMs: a decision-making tool for land managers, a means to represent the complex dynamics of rangeland ecosystems, and an effective communication tool. These diverse views of STM purposes were associated with differing perspectives concerning model development that identified five major issues in need of further development and refinement: 1) the relative importance of management practices and ecological processes in driving transitions, 2) the criteria used to define thresholds, 3) the appropriate level of model complexity, 4) the respective roles of expert knowledge and ecological data in model development, and 5) processes for model review and revision. We recommend greater dialogue among researchers and managers to further clarify STM terminology and develop standard protocols for model development and validation. Mechanisms are critically needed to assure peer review and revision of existing models so that STMs are continually updated to reflect current understanding of rangeland dynamics. Los modelos de estado y transición (SMTs) se han desarrollado para varias areas en los Estados Unidos, y representan un instrumento importante para la evaluación y el manejo de pastizales públicos o privados. Para el desarrollo de los modelos se ha invertido considerable recursos, sin embargo, se han realizado esfuerzos mínimos para evaluar la utilidad de evaluación y manejo de los SMTs. Entrevistamos a 47 profesionales en manejo de pastizales, divididos igualmente entre manejadores e investigadores, en cuatro regiones ecológicas para determinar sus conocimientos del propósito, desarrollo, fortaleza, y debilidad del SMTs para evaluar el marco del estado de SMT. Nuestros análisis identificaron tres puntos de vista principales en relación con el propósito de SMTs: una herramienta de toma de decisiones para los manejadores del recurso, una forma para representar la compleja dinámica de los ecosistemas de pastizales, y una herramienta de comunicación eficaz. Estos diversos puntos de vista, del objetivo de STM fueron asociados con diferentes perspectivas sobre el desarrollo del modelo que identifico cinco temas mayores con necesidad de refinamiento y desarrollo: 1) la importancia relativa de las prácticas de manejo y los procesos ecológicos que conducen esas transiciones, 2) los criterios utilizados para definir esos pasos, 3) el nivel apropiado de la complexidad del modelo, 4) los papeles de los datos ecológicos y conocimientos especializados en el desarrollo de los modelos, y 5) los procesos para la evaluación y revisión de los modelos. Recomendamos un mayor diálogo entre investigadores y manejadores para clarificar la terminología de SMT y desarrollar protocolos estándar para el desarrollo y validación de los modelos. Se necesitan mecanismos críticos para asegurar revisiones arbitradas así como la revisión de los modelos existentes para que los modelos de STMs se actualicen continuamente para que reflejen el conocimiento actual de la dinámica de los pastizales.

Spending degrees of freedom in a poor economy: A case study of building a sightability model for moose in northeastern Minnesota

AbstractSightability models are binary logistic‐regression models used to estimate and adjust for visibility bias in wildlife‐population surveys. Like many models in wildlife and ecology, sightability models are typically developed from small observational datasets with many candidate predictors. Aggressive model‐selection methods are often employed to choose a best model for prediction and effect estimation, despite evidence that such methods can lead to overfitting (i.e., selected models may describe random error or noise rather than true predictor–response curves) and poor predictive ability. We used moose (Alces alces) sightability data from northeastern Minnesota (2005–2007) as a case study to illustrate an alternative approach, which we refer to as degrees‐of‐freedom (df) spending: sample‐size guidelines are used to determine an acceptable level of model complexity and then a pre‐specified model is fit to the data and used for inference. For comparison, we also constructed sightability models using Akaike's Information Criterion (AIC) step‐down procedures and model averaging (based on a small set of models developed using df‐spending guidelines). We used bootstrap procedures to mimic the process of model fitting and prediction, and to compute an index of overfitting, expected predictive accuracy, and model‐selection uncertainty. The index of overfitting increased 13% when the number of candidate predictors was increased from three to eight and a best model was selected using step‐down procedures. Likewise, model‐selection uncertainty increased when the number of candidate predictors increased. Model averaging (based onR = 30 models with 1–3 predictors) effectively shrunk regression coefficients toward zero and produced similar estimates of precision to our 3‐df pre‐specified model. As such, model averaging may help to guard against overfitting when too many predictors are considered (relative to available sample size). The set of candidate models will influence the extent to which coefficients are shrunk toward zero, which has implications for how one might apply model averaging to problems traditionally approached using variable‐selection methods. We often recommend the df‐spending approach in our consulting work because it is easy to implement and it naturally forces investigators to think carefully about their models and predictors. Nonetheless, similar concepts should apply whether one is fitting 1 model or using multi‐model inference. For example, model‐building decisions should consider the effective sample size, and potential predictors should be screened (without looking at their relationship to the response) for missing data, narrow distributions, collinearity, potentially overly influential observations, and measurement errors (e.g., via logical error checks). © 2011 The Wildlife Society.

Preventing combinatorial explosion in a localist, neural network architecture using temporal synchrony

I evaluate the pros and cons of a cognitive modelling approach based strictly on localist representations by examining a sequential decision-making architecture developed to model the neural basis of problem-solving. This architecture, comprising layers of graded-activation processing units interleaved with layers of approximately binary units, seems not only amenable to distributed representations in its graded layers, but actively in need of them in order to support levels of model complexity on the scale of symbolic systems. The architecture employs a key connectionist principle: that the semantics of a representation is defined by what is connected to what. Because of this choice, however, the architecture requires a combinatorially explosive number of localist units as problem complexity increases. I discuss the possibility of preventing combinatorial explosion by binding low-level representations into high-level representations through temporal synchrony, using the same dynamics that underlie decision-making in the architecture.

Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent

Various methods exist to model a species’ niche and geographic distribution using environmental data for the study region and occurrence localities documenting the species’ presence (typically from museums and herbaria). In presence-only modelling, geographic sampling bias and small sample sizes represent challenges for many species. Overfitting to the bias and/or noise characteristic of such datasets can seriously compromise model generality and transferability, which are critical to many current applications – including studies of invasive species, the effects of climatic change, and niche evolution. Even when transferability is not necessary, applications to many areas, including conservation biology, macroecology, and zoonotic diseases, require models that are not overfit. We evaluated these issues using a maximum entropy approach (Maxent) for the shrew Cryptotis meridensis, which is endemic to the Cordillera de Mérida in Venezuela. To simulate strong sampling bias, we divided localities into two datasets: those from a portion of the species’ range that has seen high sampling effort (for model calibration) and those from other areas of the species’ range, where less sampling has occurred (for model evaluation). Before modelling, we assessed the climatic values of localities in the two datasets to determine whether any environmental bias accompanies the geographic bias. Then, to identify optimal levels of model complexity (and minimize overfitting), we made models and tuned model settings, comparing performance with that achieved using default settings. We randomly selected localities for model calibration (sets of 5, 10, 15, and 20 localities) and varied the level of model complexity considered (linear versus both linear and quadratic features) and two aspects of the strength of protection against overfitting (regularization). Environmental bias indeed corresponded to the geographic bias between datasets, with differences in median and observed range (minima and/or maxima) for some variables. Model performance varied greatly according to the level of regularization. Intermediate regularization consistently led to the best models, with decreased performance at low and generally at high regularization. Optimal levels of regularization differed between sample-size-dependent and sample-size-independent approaches, but both reached similar levels of maximal performance. In several cases, the optimal regularization value was different from (usually higher than) the default one. Models calibrated with both linear and quadratic features outperformed those made with just linear features. Results were remarkably consistent across the examined sample sizes. Models made with few and biased localities achieved high predictive ability when appropriate regularization was employed and optimal model complexity was identified. Species-specific tuning of model settings can have great benefits over the use of default settings.

A comparison of simple and complex population models to reduce uncertainty in ecological risk assessments of chemicals: example with three species of Daphnia

Ecological risk assessments (ERA) are mostly based on effects on survival (S) and fertility (F) of individuals. However, the protection goals are most often defined on the population or community levels. It has been argued that population models can be a useful link between the individual and the population in ERA. However, for population models to be efficiently and routinely used in ERA, the level of model complexity that is needed has to be clearly determined. In the present study, complex age classified matrix population models and simple 2-stage models were developed for three species of Daphnia. The population growth rate (λ) from the simple 2-stage model correlated strongly to the results of the complex matrix model, which included density dependence and temporary reductions in S and F. This shows that the information that can be provided by more complex models also can be relatively well predicted with the simpler model. The output of the complex matrix population models were also compared to the reductions in S that were used in the models. This was done because acute mortality is the most commonly used estimate of toxic effects. The results showed that λ from the 2-stage model correlated stronger to the endpoints of the matrix model than S did in all cases except for pulsed exposures, where S and λ correlated equally well.

Global-scalePwave tomography optimized for prediction of teleseismic and regional travel times for Middle East events: 2. Tomographic inversion

[1] We construct a model of three-dimensional P wave velocity structure in the crust and mantle that is global in extent, but with detailed upper mantle heterogeneities throughout the greater Middle East region. Fully three-dimensional ray tracing is employed to achieve accurate travel time predictions of P and Pn arrivals, requiring the characterization of irregular and discontinuous boundaries. Therefore, we explicitly represent undulating seismic discontinuities in the crust and upper mantle within a spherical tessellation modeling framework. The tessellation-based model architecture is hierarchical in that fine node sampling is achieved by recursively subdividing a base level tessellation. Determining the required node spacing to effectively model a given set of data is problematic, given the uneven sampling of seismic data and the differing wavelengths of actual seismic heterogeneity. To address this problem, we have developed an inversion process called Progressive Multilevel Tessellation Inversion (PMTI) that exploits the hierarchical nature of the tessellation-based design and allows the data to determine the level of model complexity. PMTI serves as an alternative to existing multiresolution approaches and robustly images regional trends while allowing localized details to emerge where resolution is sufficient. To demonstrate our complete modeling concept, we construct a velocity model based on teleseismic P travel time data for global events and regional Pn travel time data for events occurring throughout the Middle East. Input data are a product of the statistical procedure called Bayesloc that simultaneously models all components of a multievent system including event locations, origin times, and arrival times (described in the Myers et al. (2011) companion paper). The initial tomographic image provides a new glimpse of the complex upper mantle velocity anomalies associated with the convergence of the Arabian and Indian plates with Eurasia. More important for event monitoring, the model accurately predicts both teleseismic and regional travel times for events occurring within the Middle East region.

Computational Evaluation of Aortic Aneurysm Rupture Risk: What Have We Learned So Far?

In current clinical practice, aneurysm diameter is one of the primary criteria used to decide when to treat a patient with an abdominal aortic aneurysm (AAA). It has been shown that simple association of aneurysm diameter with the probability of rupture is not sufficient, and other parameters may also play a role in causing or predisposing to AAA rupture. Peak wall stress (PWS), intraluminal thrombus (ILT), and AAA wall mechanics are the factors most implicated with rupture risk and have been studied by computational risk evaluation techniques. The objective of this review is to examine these factors that have been found to influence AAA rupture. The prediction rate of rupture among computational models depends on the level of model complexity and the predictive value of the biomechanical parameters used to assess risk, such as PWS, distribution of ILT, wall strength, and the site of rupture. There is a need for simpler geometric analogues, including geometric parameters (e.g., lumen tortuosity and neck length and angulation) that correlate well with PWS, conjugated with clinical risk factors for constructing rupture risk predictive models. Such models should be supported by novel imaging techniques to provide the required patient-specific data and validated through large, prospective clinical trials.

Mechanistic Biogeochemical Model Applications for Everglades Restoration: A Review of Case Studies and Suggestions for Future Modeling Needs

Mechanistic biogeochemical model applications for freshwater wetland ecosystems are reviewed with an emphasis on applications in the Florida Everglades. Two significant human impacts on the Everglades have been hydrologic alteration and phosphorus (P) enrichment. Thus, it is important for research conducted in support of Everglades restoration to integrate understanding of the coupled effects of hydrologic and biogeochemical processes. Models are tools that can facilitate such integration, but an important challenge in model development is determining the appropriate level of model complexity. Previous wetland biogeochemical and flow modeling efforts are categorized across the spectrum of complexity from empirical and spatially aggregated to mechanistic and spatially distributed. The focus of this review is on mercury and P, as these two elements represent major environmental concerns in this ecosystem. Two case studies of coupled hydrologic and biogeochemical modeling for P transport are described in further detail to illustrate the implications of different levels of model complexity. The case study simulation results on time series TP data revealed that the mechanistic biogeochemical model with more complexity did not guarantee significantly better simulation accuracy compared to the simpler one. It is concluded that the level of model complexity should be represented appropriately based on the modeling objectives, hypotheses to be tested, and data availability. Finally, better integration between data collection and model development is encouraged as cross-fertilization between these processes may stimulate improved system understanding.

Effects of non-linear modelling of a hydromount in full vehicle simulation

The effects of different levels of model complexity for a hydromount in full vehicle simulation are investigated. In particular, the performances of the classical Kelvin-Voigt model, a standard linear lumped parameter (lever) model and an amplitude-dependent non-linear physical model with respect to their predictive capabilities are studied; thereby special attention is paid to the step from a linear to a non-linear model. The evaluation is based on comparisons with test results for crossing a single obstacle, driving on a motorway and on a rough road, respectively. Assessments in the time domain as well as by statistical means show that the non-linear model is capable of capturing high-amplitude oscillations more precisely, but that the overall differences between the simulation results are not very pronounced.