Current Practice Models Research Articles

The effects of model composition design choices on high-fidelity simulations of motoneuron recruitment and firing behaviors

Objective. Computational models often require tradeoffs, such as balancing detail with efficiency; yet optimal balance should incorporate sound design features that do not bias the results of the specific scientific question under investigation. The present study examines how model design choices impact simulation results. Approach. We developed a rigorously-validated high-fidelity computational model of the spinal motoneuron pool to study three long-standing model design practices which have yet to be examined for their impact on motoneuron recruitment, firing rate, and force simulations. The practices examined were the use of: (1) generic cell models to simulate different motoneuron types, (2) discrete property ranges for different motoneuron types, and (3) biological homogeneity of cell properties within motoneuron types. Main results. Our results show that each of these practices accentuates conditions of motoneuron recruitment based on the size principle, and minimizes conditions of mixed and reversed recruitment orders, which have been observed in animal and human recordings. Specifically, strict motoneuron orderly size recruitment occurs, but in a compressed range, after which mixed and reverse motoneuron recruitment occurs due to the overlap in electrical properties of different motoneuron types. Additionally, these practices underestimate the motoneuron firing rates and force data simulated by existing models. Significance. Our results indicate that current modeling practices increase conditions of motoneuron recruitment based on the size principle, and decrease conditions of mixed and reversed recruitment order, which, in turn, impacts the predictions made by existing models on motoneuron recruitment, firing rate, and force. Additionally, mixed and reverse motoneuron recruitment generated higher muscle force than orderly size motoneuron recruitment in these simulations and represents one potential scheme to increase muscle efficiency. The examined model design practices, as well as the present results, are applicable to neuronal modeling throughout the nervous system.

Building equity in: strategies for integrating equity into modelling for a 1.5°C world.

Emission pathways consistent with limiting temperature increase to 1.5°C raise pressing questions from an equity perspective. These pathways would limit impacts and benefit vulnerable communities but also present trade-offs that could increase inequality. Meanwhile, rapid mitigation could exacerbate political debates in which equity has played a central role. In this paper, we first develop a set of elements we suggest are essential for evaluating the equity implications of policy actions consistent with 1.5°C. These elements include (i) assess climate impacts, adaptation, loss and damage; (ii) be sensitive to context; (iii) compare costs of mitigation and adaptation policy action; (iv) incorporate human development and poverty; (v) integrate inequality dynamics; and (vi) be clear about normative assumptions and responsive to users. We then assess the ability of current modelling practices to address each element, focusing on global integrated assessment models augmented by national modelling and scenarios. We find current practices face serious limitations across all six dimensions although the severity of these varies. Finally, based on our assessment we identify strategies that may be best suited for enabling us to generate insights into each of the six elements in the context of assessing pathways for a 1.5°C world.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

What does interdisciplinarity look like in practice: Mapping interdisciplinarity and its limits in the environmental sciences

In this paper we take a close look at current interdisciplinary modeling practices in the environmental sciences, and suggest that closer attention needs to be paid to the nature of scientific practices when investigating and planning interdisciplinarity. While interdisciplinarity is often portrayed as a medium of novel and transformative methodological work, current modeling strategies in the environmental sciences are conservative, avoiding methodological conflict, while confining interdisciplinary interactions to a relatively small set of pre-existing modeling frameworks and strategies (a process we call crystallization). We argue that such practices can be rationalized as responses in part to cognitive constraints which restrict interdisciplinary work. We identify four salient integrative modeling strategies in environmental sciences, and argue that this crystallization, while contradicting somewhat the novel goals many have for interdisciplinarity, makes sense when considered in the light of common disciplinary practices and cognitive constraints. These results provide cause to rethink in more concrete methodological terms what interdisciplinarity amounts to, and what kinds of interdisciplinarity are obtainable in the environmental sciences and elsewhere.

Scientific white paper on concentration-QTc modeling.

The International Council for Harmonisation revised the E14 guideline through the questions and answers process to allow concentration-QTc (C-QTc) modeling to be used as the primary analysis for assessing the QTc interval prolongation risk of new drugs. A well-designed and conducted QTc assessment based on C-QTc modeling in early phase 1 studies can be an alternative approach to a thorough QT study for some drugs to reliably exclude clinically relevant QTc effects. This white paper provides recommendations on how to plan and conduct a definitive QTc assessment of a drug using C-QTc modeling in early phase clinical pharmacology and thorough QT studies. Topics included are: important study design features in a phase 1 study; modeling objectives and approach; exploratory plots; the pre-specified linear mixed effects model; general principles for model development and evaluation; and expectations for modeling analysis plans and reports. The recommendations are based on current best modeling practices, scientific literature and personal experiences of the authors. These recommendations are expected to evolve as their implementation during drug development provides additional data and with advances in analytical methodology.

Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: A Bayesian Network approach.

Urban water pollution poses risks of waterborne infectious diseases. Therefore, in order to improve urban liveability, effective pollution mitigation strategies are required underpinned by predictions generated using water quality models. However, the lack of reliability in current modelling practices detrimentally impacts planning and management decision making. This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate indicators of human health risk, namely, turbidity, total nitrogen and fats/oils. Expert knowledge was found to be of critical importance in assessing the interdependent relationships between health risk indicators and influential factors. The spatial variability maps of health risk indicators developed enabled the initial identification of high risk areas in which flooding was found to be the most significant influential factor in relation to human health risk. Surprisingly, population density was found to be less significant in influencing health risk indicators. These high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. It was evident that decision making in relation to the design of pollution mitigation strategies needs to account for the impact of landscape characteristics on water quality, which can be related to risk to human health.

Crop rotations and crop residues are relevant parameters for agricultural carbon footprints

Agriculture is the key for achieving the United Nations sustainable development goals: food security and climate action. To achieve these targets “climate-smart” agricultural practices need to be developed. Life cycle assessment and product carbon footprints are well established and internationally recognized tools to assist the process of improving environmental performance. However, there is room for methodological improvement of agricultural life cycle assessments and product carbon footprints. For agronomists, it is widely known that crop rotations and crop residues do fulfill important agronomic functions, but they are not adequately represented in current life cycle assessment and product carbon footprint modeling practice. New methods tested in this study allow the inclusion of crop rotation effects and crop residues as co-products, whilst keeping at the same time the product focus. Product carbon footprints are calculated with and without consideration of these effects; results are compared. If crop rotations are considered, wheat bread, cow milk, and rapeseed biodiesel have lower product carbon footprints (− 11, − 22, and − 16%, respectively). The product carbon footprint of straw bioethanol significantly increases (+ 80%) when considering straw as an agricultural co-product instead of as waste. Ignoring crop rotation effects underestimates the annual greenhouse gas savings of EU-28 rapeseed biodiesel by 1.67 million t CO2e and 20%, respectively. Here, we demonstrate for the first time that crop rotations and straw harvest should be considered for the product carbon footprints of bread, milk, and first- and second-generation biofuels. Since crop rotations and straw harvest are performed worldwide, the findings are relevant to all regions in the world. Comparing crop rotations and identifying climate-smart agricultural practices without losing the production orientation are key challenges for environmental assessments of agriculture in order to achieve the challenging combination of the food security and climate action sustainable development goals.

Land Use and Land-use Changes in Life Cycle Assessment: Green Modelling or Black Boxing?

The assessment of Land Uses and Land-use Changes (LULUC) impacts has become increasingly complex. Sophisticated modelling tools such as Life Cycle Assessment (LCA) are employed to capture both direct and indirect damages. However, quantitative assessments are often incomplete, dominated by environmental aspects. Land uses are a multidisciplinary matter and environmental and sustainable development policies intertwine. Yet, LCAs mostly focus on environmental impacts excluding socioeconomic implications of land occupation. This paper investigates the limitations of current LULUC modelling practices in LCA. Common LCA assumptions harbor value choices reflect a post-positivist epistemology that are often non-transparent to e.g. policymakers. They particularly influence the definition of the functional unit, the reference system and system boundaries, among other LCA methodological choices. Consequently, results informing land policies may be biased towards determined development strategies or hide indirect effects and socioeconomic damages caused by large-scale land acquisitions, such as violation of tenure rights, speculation and displacement. Quantitative assessments of LULUC impacts are certainly useful but should holistically encompass both direct and indirect impacts concerning the environmental and the social science dimension of LULUC. An epistemological shift towards a dialectic approach would facilitate the integration of multiple tools and methods and a critical interpretation of results.

Blueprint for a coupled model of sedimentology, hydrology, and hydrogeology in streambeds

AbstractThe streambed constitutes the physical interface between the surface and the subsurface of a stream. Across all spatial scales, the physical properties of the streambed control surface water‐groundwater interactions. Continuous alteration of streambed properties such as topography or hydraulic conductivity occurs through erosion and sedimentation processes. Recent studies from the fields of ecology, hydrogeology, and sedimentology provide field evidence that sedimentological processes themselves can be heavily influenced by surface water‐groundwater interactions, giving rise to complex feedback mechanisms between sedimentology, hydrology, and hydrogeology. More explicitly, surface water‐groundwater exchanges play a significant role in the deposition of fine sediments, which in turn modify the hydraulic properties of the streambed. We explore these feedback mechanisms and critically review the extent of current interaction between the different disciplines. We identify opportunities to improve current modeling practices. For example, hydrogeological models treat the streambed as a static rather than a dynamic entity, while sedimentological models do not account for critical catchment processes such as surface water‐groundwater exchange. We propose a blueprint for a new modeling framework that bridges the conceptual gaps between sedimentology, hydrogeology, and hydrology. Specifically, this blueprint (1) fully integrates surface‐subsurface flows with erosion, transport, and deposition of sediments and (2) accounts for the dynamic changes in surface elevation and hydraulic conductivity of the streambed. Finally, we discuss the opportunities for new research within the coupled framework.

Thermal-hydraulic performance of metal foam heat exchangers under dry operating conditions

High porosity metal foams with novel thermal, mechanical, electrical, and acoustic properties are being more widely adopted for application. Due to their large surface-area-to-volume ratio and complex structure which induces better fluid mixing, boundary layer restarting and wake destruction, they hold promise for heat transfer applications. In this paper, the thermal-hydraulic performance of open-cell aluminum metal foam heat exchanger has been evaluated. The impact of flow conditions and metal foam geometry on the heat transfer coefficient and gradient have been investigated. Metal foam heat exchanger with same geometry (face area, flow depth and fin dimensions) consisting of four different type of metal foams have been built for the study. Experiments are conducted in a closed-loop wind tunnel at different flow rate under dry operating condition. Metal foams with a smaller pore size (40PPI) have a larger heat transfer coefficient compared to foams with a larger pore size (5PPI). However, foams with larger pores result in relatively smaller pressure gradients. Current thermal-hydraulic modeling practices have been reviewed and potential issues have been identified. Permeability and inertia coefficients are determined and compared to data reported in open literature. On the basis of the new experimental results, correlations are developed relating the foam characteristics and flow conditions through the friction factor f and the Colburn j factor.

Assessment of Data Analysis Practices in Debris Flow Modeling

Assessment of Data Analysis Practices in Debris Flow Modeling Debris flows can seriously damage infrastructure and potentially be responsible for the loss of life. Accurate estimates of debris volumes are necessary to design debris basins. The design accuracy of debris basins is currently limited by practices used in collecting data and then in the use of the data for model development. The goal of this research was to assess two current practices in debris flow collection and modeling. The specific objectives were: (1) to demonstrate that spurious modeling of debris production rates is not a good practice for estimating debris volumes; (2) to show that watershed burn can significantly influence the generation of debris volumes; and (3) to show the effect of the data collection interval on the accuracy of predicting debris volumes. Models to predict expected debris volumes for five regions of Southern California were developed using data from 35 existing debris basins. The correlation coefficients ranged from 0.26 to 0.91. Simulations were conducted to analyze the following data collection intervals: 1-year, 2-year, and random, with the following record lengths: 15, 30, 50, and 70 years. Results showed that both the data collection interval and the record length influence the magnitude of predicted debris volumes. These analyses also showed that prediction accuracy is greatly affected by current data collection and modeling practices.

International survey on current occupant modelling approaches in building performance simulation

It is not evident that practitioners have kept pace with latest research developments in building occupant behaviour modelling; nor are the attitudes of practitioners regarding occupant behaviour modelling well understood. In order to guide research and development efforts, researchers, policy-makers, and software developers require a better understanding of current practice and acceptance of occupant modelling. This paper provides results, analysis, and discussion of the results of a 36-question international survey on current occupant modelling practice and attitudes in building performance simulation. In total, 274 valid responses were collected from BPS users (practitioners, educators, and researchers) from 37 countries. The results indicate that most assumptions made about occupants vary widely and are considerably simpler than what has been observed in reality. Most participants cited lack of time or understanding as their primary reason for not delving deeply into occupant modelling, but responded that they are receptive to further training.

Sustainable Sizing.

To provide a review of sustainable sizing practices that reduce waste, increase sales, and simultaneously produce safer, better fitting, accommodating products. Sustainable sizing involves a set of methods good for both the environment (sustainable environment) and business (sustainable business). Sustainable sizing methods reduce (1) materials used, (2) the number of sizes or adjustments, and (3) the amount of product unsold or marked down for sale. This reduces waste and cost. The methods can also increase sales by fitting more people in the target market and produce happier, loyal customers with better fitting products. This is a mini-review of methods that result in more sustainable sizing practices. It also reviews and contrasts current statistical and modeling practices that lead to poor fit and sizing. Fit-mapping and the use of cases are two excellent methods suited for creating sustainable sizing, when real people (vs. virtual people) are used. These methods are described and reviewed. Evidence presented supports the view that virtual fitting with simulated people and products is not yet effective. Fit-mapping and cases with real people and actual products result in good design and products that are fit for person, fit for purpose, with good accommodation and comfortable, optimized sizing. While virtual models have been shown to be ineffective for predicting or representing fit, there is an opportunity to improve them by adding fit-mapping data to the models. This will require saving fit data, product data, anthropometry, and demographics in a standardized manner. For this success to extend to the wider design community, the development of a standardized method of data collection for fit-mapping with a globally shared fit-map database is needed. It will enable the world community to build knowledge of fit and accommodation and generate effective virtual fitting for the future. A standardized method of data collection that tests products' fit methodically and quantitatively will increase our predictive power to determine fit and accommodation, thereby facilitating improved, effective design. These methods apply to all products people wear, use, or occupy.

Overview of current regional and local scale air quality modelling practices: Assessment and planning tools in the EU

The 2008 European Air Quality Directive (AQD) (2008/50/EC) encourages the use of models in combination with monitoring in a range of applications. It also requires Member States to design appropriate air quality plans for zones where the air quality does not comply with the AQD limit values. In order to cope with these various elements, a wide range of different modeling methods have been developed and applied by EU Member States in the last decade to assess the effects of local and regional emission abatement policy options on air quality and human health. However, an overall review of the methodologies that are used in different countries to compile local and regional air quality plans has not been performed so far. Such a review has been the objective of the APPRAISAL EU FP7 project with the main goal to identify methodologies and their limitations and to propose possible key areas to be addressed by research and innovation on the basis of this review. To fulfill these objectives, a structured online database of methodologies has been developed in collaboration with experts involved in the design of air quality plans (AQP). The current work relies on the APPRAISAL database which currently totals 59 contributions from 13 Member States. In this paper we summarize the outcome of the APPRAISAL project with respect to the review of current Integrated Assessment Modeling practices.

Improved Model Formulations for Multiperiod Hydrogen Network Designs

Operating cost reduction and/or air pollution abatement via hydrogen integration is a research issue that has recently attracted considerable attention in the petroleum refining industries. Although a number of mathematical programming models have been developed to generate the optimal hydrogen distribution schemes, there is still room for further improvement. In particular, the primary deficiencies in current modeling practices can be attributed to (1) unreasonable unit models of the hydrogen users and producers and (2) incomprehensive design considerations. The conventional models of hydrogen users (such as the hydrotreater and hydrocracker) were usually formulated according to fixed throughputs and also constant feed and product concentrations. On the basis of the shortcut calculation method proposed in this study, not only these inlet and outlet flow rates and concentrations can be treated as decision variables but also their interactions can be characterized with more consistent material-balance constraints. On the other hand, because every hydrogen producer, e.g., the steam reforming plant, was regarded only as a simple hydrogen source in the past, the more rigorous models of its embedded units are built and incorporated in the improved mathematical program. As a result of these modifications, better design options can be identified in the present study. To ensure comprehensive design considerations, all often-encountered seasonal variations in model parameters and options to add extra compressors, purifiers, and fuel cells are considered in a novel multiperiod formulation. Furthermore, as an alternative to numerically solving this model, a systematic timesharing algorithm is also devised to manually integrate the conventional single-period designs to form a less economical but more flexible network structure for operations in all periods. Finally, extensive case studies have been carried out to test the proposed design methods, and three examples are reported.

Soil property variation mapping through data mining of soil category maps

AbstractSpatial information on soil properties is an important input to hydrological models. In current hydrological modelling practices, soil property information is often derived from soil category maps by the linking method in which a representative soil property value is linked to each soil polygon. Limited by the area‐class nature of soil category maps, the derived soil property variation is discontinuous and less detailed than high resolution digital terrain or remote sensing data. This research proposed dmSoil, a data‐mining‐based approach to derive continuous and spatially detailed soil property information from soil category maps. First, the soil–environment relationships are extracted through data mining of a soil map. The similarity of the soil at each location to different soil types in the soil map is then estimated using the mined relationships. Prediction of soil property values at each location is made by combining the similarities of the soil at that location to different soil types and the representative soil property values of these soil types. The new approach was applied in the Raffelson Watershed and Pleasant Valley in the Driftless Area of Wisconsin, United States to map soil A horizon texture (in both areas) and depth to soil C horizon (in Pleasant Valley). The property maps from the dmSoil approach capture the spatial gradation and details of soil properties better than those from the linking method. The new approach also shows consistent accuracy improvement at validation points. In addition to the improved performances, the inputs for the dmSoil approach are easy to prepare, and the approach itself is simple to deploy. It provides an effective way to derive better soil property information from soil category maps for hydrological modelling. Copyright © 2014 John Wiley & Sons, Ltd.

Current modeling practice may lead to falsely high benchmark dose estimates

Current modeling practice may lead to falsely high benchmark dose estimates

Agricultural adaptation to climate change in rich and poor countries: Current modeling practice and potential for empirical contributions

In this paper we discuss the scope of the adaptation challenge facing world agriculture in the coming decades. Due to rising temperatures throughout the tropics, pressures for adaptation will be greatest in some of the poorest parts of the world where the adaptive capacity is least abundant. We discuss both autonomous (market driven) and planned adaptations, distinguishing: (a) those that can be undertaken with existing technology, (b) those that involve development of new technologies, and (c) those that involve institutional/market and policy reforms. The paper then proceeds to identify which of these adaptations are currently modeled in integrated assessment studies and related analyses at global scale. This, in turn, gives rise to recommendations about how these models should be modified in order to more effectively capture climate change adaptation in the farm and food sector. In general, we find that existing integrated assessment models are better suited to analyzing adaptation by relatively well-endowed producers operating in market-integrated, developed countries. They likely understate climate impacts on agriculture in developing countries, while overstating the potential adaptations. This is troubling, since the need for adaptation will be greatest amongst the lower income producers in the poorest tropical countries. This is also where policies and public investments are likely to have the highest payoff. We conclude with a discussion of opportunities for improving the empirical foundations of integrated assessment modeling with an emphasis on the poorest countries.

Current modeling practice may lead to falsely high benchmark dose estimates

Benchmark dose (BMD) modeling is increasingly used as the preferred approach to define the point-of-departure for health risk assessment of chemicals. As data are inherently variable, there is always a risk to select a model that defines a lower confidence bound of the BMD (BMDL) that, contrary to expected, exceeds the true BMD. The aim of this study was to investigate how often and under what circumstances such anomalies occur under current modeling practice. Continuous data were generated from a realistic dose–effect curve by Monte Carlo simulations using four dose groups and a set of five different dose placement scenarios, group sizes between 5 and 50 animals and coefficients of variations of 5–15%. The BMD calculations were conducted using nested exponential models, as most BMD software use nested approaches. “Non-protective” BMDLs (higher than true BMD) were frequently observed, in some scenarios reaching 80%. The phenomenon was mainly related to the selection of the non-sigmoidal exponential model (Effect=a·eb·dose). In conclusion, non-sigmoid models should be used with caution as it may underestimate the risk, illustrating that awareness of the model selection process and sound identification of the point-of-departure is vital for health risk assessment.

Biological variability in biomechanical engineering research: Significance and meta-analysis of current modeling practices

Biological systems are characterized by high levels of variability, which can affect the results of biomechanical analyses. As a review of this topic, we first surveyed levels of variation in materials relevant to biomechanics, and compared these values to standard engineered materials. As expected, we found significantly higher levels of variation in biological materials. A meta-analysis was then performed based on thorough reviews of 60 research studies from the field of biomechanics to assess the methods and manner in which biological variation is currently handled in our field. The results of our meta-analysis revealed interesting trends in modeling practices, and suggest a need for more biomechanical studies that fully incorporate biological variation in biomechanical models and analyses. Finally, we provide some case study example of how biological variability may provide valuable insights or lead to surprising results. The purpose of this study is to promote the advancement of biomechanics research by encouraging broader treatment of biological variability in biomechanical modeling.

Development and application of near-well multiphase upscaling for forecasting of heavy oil primary production

Abstract Near-well effects can have a strong impact on reservoir flow. Current reservoir modeling practice often uses coarse-scale flow simulation models, which may lead to biased results, compared with fine-scale models. In this work, we extend and apply a recently developed near-well multiphase flow upscaling technique to the coarse-scale simulation of heavy-oil primary production. For heavy oils, oil viscosity is a strong function of pressure when the pressure is below the bubble point. Therefore, the upscaled mobility functions (from near-well multiphase upscaling) depend on both pressure and saturation, which cannot be directly input to general purpose reservoir simulators. This is very different than the upscaled mobility functions for typical black-oil fluids, in which oil viscosity does not vary significantly with pressure. Accordingly, the upscaled mobility functions are often equivalent to upscaled relative permeabilities (as functions of saturation only). In this work, we develop two procedures to derive either the upscaled relative permeability or viscosity functions from the phase mobility functions, thus decoupling the dependency on pressure and saturation. It is found that the upscaled oil viscosity provides more accurate predictions than the upscaled relative permeabilities, especially at early time. This is because that the rapid change of pressure at the early stage of production is captured sufficiently in the upscaled oil viscosity (as a function of pressure). The use of upscaled viscosity function in multiphase upscaling is new, and has not been presented in previous studies. We also introduce a grouping technique to reduce the number of upscaled flow functions in coarse-scale models. This is based on an observation that there is a strong correlation between the upscaled flow functions and the coarse-scale well-block permeabilities. The proposed methods are applied to realistic models from heavy-oil fields. For cases considered, the near-well multiphase flow upscaling considerably improves upon the standard coarse-scale models. The use of upscaled relative permeability and viscosity functions, as well as the grouping of upscaled flow functions, provides practical applicability of the proposed method.