Bottom-up Modeling Approach Research Articles

Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions.

Opportunistic fungal pathogens can cause bloodstream infection and severe sepsis upon entering the blood stream of the host. The early immune response in human blood comprises the elimination of pathogens by antimicrobial peptides and innate immune cells, such as neutrophils or monocytes. Mathematical modeling is a predictive method to examine these complex processes and to quantify the dynamics of pathogen-host interactions. Since model parameters are often not directly accessible from experiment, their estimation is required by calibrating model predictions with experimental data. Depending on the complexity of the mathematical model, parameter estimation can be associated with excessively high computational costs in terms of run time and memory. We apply a strategy for reliable parameter estimation where different modeling approaches with increasing complexity are used that build on one another. This bottom-up modeling approach is applied to an experimental human whole-blood infection assay for Candida albicans. Aiming for the quantification of the relative impact of different routes of the immune response against this human-pathogenic fungus, we start from a non-spatial state-based model (SBM), because this level of model complexity allows estimating a priori unknown transition rates between various system states by the global optimization method simulated annealing. Building on the non-spatial SBM, an agent-based model (ABM) is implemented that incorporates the migration of interacting cells in three-dimensional space. The ABM takes advantage of estimated parameters from the non-spatial SBM, leading to a decreased dimensionality of the parameter space. This space can be scanned using a local optimization approach, i.e., least-squares error estimation based on an adaptive regular grid search, to predict cell migration parameters that are not accessible in experiment. In the future, spatio-temporal simulations of whole-blood samples may enable timely stratification of sepsis patients by distinguishing hyper-inflammatory from paralytic phases in immune dysregulation.

Disentangling diversification in supply chain networks

We propose a model for the quantification of business disruption risk in a global supply chain network. To calculate the loss distribution induced by supply chain disruptions for a focal firm, we apply a bottom-up modeling approach. On the firm level, we model production disruptions of various hazard events in reduced form. We incorporate the network structure explicitly and define the loss propagation between the firms. Via Monte Carlo simulation, we analyze the effects of different model specifications and network structures on the loss distribution of the focal firm. We show that diversification effects can lead to counterintuitive results when we consider the network structure and the correlations of hazard events. Our methodology and findings enable more informed and transparent decisions for supply chain design.

Species survival emerge from rare events of individual migration.

Ecosystems greatly vary in their species composition and interactions, yet they all show remarkable resilience to external influences. Recent experiments have highlighted the significant effects of spatial structure and connectivity on the extinction and survival of species. It has also been emphasized lately that in order to study extinction dynamics reliably, it is essential to incorporate stochasticity, and in particular the discrete nature of populations, into the model. Accordingly, we applied a bottom-up modeling approach that includes both spatial features and stochastic interactions to study survival mechanisms of species. Using the simplest spatial extension of the Lotka-Volterra predator-prey model with competition, subject to demographic and environmental noise, we were able to systematically study emergent properties of this rich system. By scanning the relevant parameter space, we show that both survival and extinction processes often result from a combination of habitat fragmentation and individual rare events of recolonization.

Energy saving potential of information and communication technology

Information and communication technology (ICT) strongly contributed to the rising global electricity demand in past years. On the other hand, technology dynamics produces a large potential for energy saving which is being discussed under the heading ‘green IT’. In this paper, we use a bottom-up stock modelling approach to determine the net impact of these opposite trends on the future development of electricity consumption in Germany. We show that there is a high technological improvement potential to reduce electricity consumption for ICT equipment, which can at least slow down the consumption increase or even stabilise consumption until 2020. However, whether these saving potentials will be exploited, also depends on the policies which countries introduce to stimulate the uptake of very efficient products and network solutions.

An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems.

Computational modelling of biochemical systems based on top-down and bottom-up approaches has been well studied over the last decade. In this research, after illustrating how to generate atomic components by a set of given reactants and two user pre-defined component patterns, we propose an integrative top-down and bottom-up modelling approach for stepwise qualitative exploration of interactions among reactants in biochemical systems. Evolution strategy is applied to the top-down modelling approach to compose models, and simulated annealing is employed in the bottom-up modelling approach to explore potential interactions based on models constructed from the top-down modelling process. Both the top-down and bottom-up approaches support stepwise modular addition or subtraction for the model evolution. Experimental results indicate that our modelling approach is feasible to learn the relationships among biochemical reactants qualitatively. In addition, hidden reactants of the target biochemical system can be obtained by generating complex reactants in corresponding composed models. Moreover, qualitatively learned models with inferred reactants and alternative topologies can be used for further web-lab experimental investigations by biologists of interest, which may result in a better understanding of the system.

Deep CO2 emission reductions in a global bottom-up model approach

Most studies that explore deep GHG emission reduction scenarios assume that climate goals are reached by implementing least-cost emission mitigation options, typically by implementing a global carbon tax. Although such a method provides insight into total mitigation costs, it does not provide much information about how to achieve a transition towards a low-carbon energy system, which is of critical importance to achieving ambitious climate targets. To enable sensible deep emission reduction strategies, this study analysed the effectiveness of 16 specific mitigation measures on a global level up to 2050, by using an energy-system simulation model called TIMER. The measures range from specific energy efficiency measures, like banning traditional light bulbs and subsidizing electric vehicles, to broader policies like introducing a carbon tax in the electricity sector. All measures combined lead to global CO2 emission reductions ranging between 39% and 73% compared to baseline by 2050, depending on the inclusion of sectoral carbon taxes and the availability of carbon capture and storage (CCS) and nuclear power. Although the effectiveness of the measures differs largely across regions, this study indicates that measures aimed at stimulating low-carbon electricity production result in the highest reductions in all regions.Policy relevanceThe results of the calculations can be used to evaluate the effects of individual climate change mitigation measures and identify priorities in discussions on global and regional policies. The type of fragmented policy scenarios presented here could provide a relevant bottom-up alternative to cost-optimal implementation of policies driven by a carbon tax. We identify overlapping and even counter-productive climate policy measures through an analysis that presents the policy effectiveness by region, and by sector. The set of 16 policy measures addresses the largest emitting sectors and represents options that are often discussed as part of planned policies.

Turkey’s Strategic Energy Efficiency Plan – An ex ante impact assessment of the residential sector

Turkey’s energy demand has been growing by 4.5% per year over the last decade. As a reaction to this, the Turkish government has implemented the Strategic Energy Efficiency Plan (SEEP), which provides a guideline for energy efficiency policies in all sectors.The aim of this study is to analyse the potential of the SEEP on final energy demand in the Turkish residential sector until 2030. Three scenarios are developed based on a detailed bottom-up modelling approach using a vintage stock model to simulate the energy demand of heating systems and appliances.The results show a decreasing final energy demand in the reference scenario from about 944PJ in 2008 to 843PJ in 2030. This reflects a structural break, which is mainly caused by a high building demolition rate and low efficiency in the existing building stock. The SEEP achieves additional savings of around 111PJ until 2030, while a scenario with even higher efficiency shows further savings of 91PJ. Electricity demand increases in all scenarios – mainly due to growing ownership rates of appliances. The SEEP will achieve around 10TWh of electricity savings in 2030 compared to the reference scenario, mainly through more ambitious end-use standards.

A Fixed-Income Market View of Mortgage REIT Valuations

This article discusses the use of mortgage-backed securities and fixed-income derivatives analytics in order to model agency mortgage real estate investment trust (REIT) holdings in more detail than standard equity market practice. The author shows that book value projections derived from a full bottom-up modeling approach are quite accurate. He also computes such metrics as duration, convexity, and basis risk of mortgage REITs, and suggests hedging strategies. Finally the author explores performance attribution and how to fundamentally explain the large yields observed on these stocks. <b>TOPICS:</b>MBS and residential mortgage loans, derivatives applications, real estate

Structural Default Modeling: A Hybrid Based Approach

This article discusses the use of mortgage-backed securities and fixed-income derivatives analytics in order to model agency mortgage real estate investment trust (REIT) holdings in more detail than standard equity market practice. The author shows that book value projections derived from a full bottom-up modeling approach are quite accurate. He also computes such metrics as duration, convexity, and basis risk of mortgage REITs, and suggests hedging strategies. Finally the author explores performance attribution and how to fundamentally explain the large yields observed on these stocks. <b>TOPICS:</b>Fixed income and structured finance, fixed-income portfolio management, portfolio theory

Development of Low-Voltage Load Models for the Residential Load Sector

A bottom-up modeling approach is presented that uses a Markov chain Monte Carlo (MCMC) method to develop demand profiles. The demand profiles are combined with the electrical characteristics of the appliance to create detailed time-varying models of residential loads suitable for the analysis of smart grid applications and low-voltage (LV) demand-side management. The results obtained demonstrate significant temporal variations in the electrical characteristics of LV customers that are not captured by existing load profile or load model development approaches. The software developed within this work is made freely available for use by the community.

District Energy Use Patterns and Potential Savings in the Built Environment: Case Study of Two Districts in Seoul, South Korea

Energy efficiency is vital to improve energy security, environmental and social sustainability, and economic performance. Improved energy efficiency also mitigates climate change by lowering greenhouse gas (GHG) emissions. Buildings are the single largest industrial consumer of energy and are therefore key to understanding and analyzing energy consumption patterns and the opportunities for saving energy at the district level in urban environments. This study focused on two representative boroughs in the major metropolitan area of Seoul, South Korea as a case study: Gandong-gu, a typical residential district, and Jung-gu, a typical commercial district. The sources of the energy supplied to the boroughs were determined and consumption patterns in different industry sectors in Seoul used to identify current patterns of energy consumption. The study analyzed the energy consumption patterns for five different building categories and four different sectors in the building using a bottom-up energy modeling approach. Electricity and gas consumption patterns were recorded for different building categories and monthly ambient temperatures in the two boroughs. Finally, a logarithmic equation was developed to describe the correlation between commercial activity and cooling energy intensity in Jung-gu, the commercial district. Based on these results, recommendations are made regarding the current energy consumption patterns at the district level and government energy policies are suggested to reduce energy consumption and, hence, greenhouse gas emissions, in both commercial and residential buildings.

Pharmacometrics in Pregnancy: An Unmet Need

Pregnant women and their fetuses are orphan populations with respect to the safety and efficacy of drugs. Physiological and absorption, distribution, metabolism, and excretion (ADME) changes during pregnancy can significantly affect drug pharmacokinetics (PK) and may necessitate dose adjustment. Here, the specific aspects related to the design, execution, and analysis of clinical studies in pregnant women are discussed, underlining the unmet need for top-down pharmacometrics analyses and bottom-up modeling approaches. The modeling tools that support data analysis for the pregnancy population are reviewed, with a focus on physiologically based pharmacokinetics (PBPK) and population pharmacokinetics (POP-PK). By integrating physiological data, preclinical data, and clinical data (e.g., via POP-PK) to quantify anticipated changes in the PK of drugs during pregnancy, the PBPK approach allows extrapolation beyond the previously studied model drugs to other drugs with well-characterized ADME characteristics. Such a systems pharmacology approach can identify drugs whose PK may be altered during pregnancy, guide rational PK study design, and support dose adjustment for pregnant women.

Beyond preference: Modelling segregation under regulation

Segregation models often focus on private racial preference but overlook the institutional context. This paper represents an effort to move beyond the preference centricity. In this paper, an ideal Pigovian regulatory intervention is emulated and added into Schelling’s (1971) classic spatial proximity model of racial segregation, with an aim to preserve collective welfare against the negative externalities induced by the changing local racial compositions after individual relocations. A key discovery from a large number of cellular automata is that the Pigovian regulation tends to result in less segregated but also less efficient (in terms of aggregate utility) residential patterns than laissez faire. This finding, albeit from a highly stylized model, bears intellectual relations to an important practical question: What are the potential racial effects of Pigovian local planning interventions, such as financially motivated anti-density zoning or the collection of a development impact fee? On top of its modest policy implications, this paper demonstrates a bottom-up computational modelling approach to reconcile the preference-based and institution-orientated academic perspectives regarding racial residential segregation.

Multi-Agent Based Modeling Simulation about VANET

The self-organization characteristics and the interaction between a large numbers of self-organizing vehicles are complexity, to obtain a more accurate model of vehicular Ad-hoc network (VANET) and obtain a more profound comprehension of the complex behavior working mechanism of the vehicle in the VANET environment multi-agent based and bottom-up modeling approach is proposed here. It aims to describe the dynamics of VANET caused by the different behaviors of vehicular. The simulation tool for vehicular misbehaviors is developed with multi-agent. It aims to and be able to effectively reproduce the real VANET scene. Though the multi-agent based modeling the emergent behavior and sudden existing behaviors of VANET entities are well reflected.

A bottom-up stochastic model to predict building occupants' time-dependent activities

A bottom-up modelling approach together with a set of calibration methodologies is presented to predict residential building occupants' time-dependent activities, for use in dynamic building simulations. The stochastic model to predict activity chains is calibrated using French time-use survey data (of 1998/1999), based on three types of time-dependent quantities: (i) the probability to be at home, (ii) the conditional probability to start an activity whilst being at home, and (iii) the probability distribution function for the duration of that activity. The behaviour of the individual agents in the model is first calibrated using a generic approach, where every individual is assumed to behave the same. A refinement is then presented to account for variations in the behaviours of sub-populations, having specific individual characteristics. Furthermore, a statistical approach is introduced for the modelling of transitions between two successive activity types as a Markov process. The models are then validated using a cross-validation technique, and their predictive performance is compared at an individual level, as well as for aggregated (sub-)populations.

Model-Based Identification and Analysis of the Energy Saving Potential in Batch Chemical Processes

Optimization of energy consumption for reducing the relevant costs and environmental impacts is constantly gaining attention in chemical batch production. Existing methodologies focus on heat integration considering scheduling constraints and typically result in trade-offs between capital investment and operational costs. However, in multipurpose batch plants, even the allocation of energy flows and the consistent operation according to production recipes pose a great challenge due to batch-to-batch variability and lack of energy utility consumption meters. This paper utilizes a bottom-up modeling approach for energy utility consumption and proposes a method for model-based identification of the energy saving potential in chemical batch plants. The bottom-up models can accurately track the energy utility consumption at various production levels and are used as soft sensors for energy efficiency analysis studies. In this context, a set of energy key performance indicators (EKPIs) is proposed for quantifying efficiency in energy use, and an energy saving potential index (ESPI) based on historical plant performance serves as a shortcut method in the case of missing or inaccurate production recipes. The methodology is applied to an industrial multipurpose batch plant for specialty chemicals, exemplifying the obtained efficiency results and targeting energy saving potential for steam consumption.

Challenges for land system science

While considerable progress has been made in understanding land use change, land system science continues to face a number of grand challenges. This paper discusses these challenges with a focus on empirical land system studies, land system modelling and the analysis of future visions of land system change. Contemporary landscapes are contingent outcomes of past and present patterns, processes and decisions. Thus, empirical analysis of past and present land-use change has an important role in providing insights into the socio-economic and ecological processes that shape land use transitions. This is especially important with respect to gradual versus rapid land system dynamics and in understanding changes in land use intensity. Combining the strengths of empirical analysis with multi-scale modelling will lead to new insights into the processes driving land system change. New modelling methods that combine complex systems thinking at a local level with macro-level economic analysis of the land system would reconcile the multi-scale dynamics currently encapsulated in bottom-up and top-down modelling approaches. Developments in land use futures analysis could focus on integrating explorative scenarios that reflect possible outcomes with normative visions that identify desired outcomes. Such an approach would benefit from the broad and in-depth involvement of stakeholders in order to link scientific findings to political and societal decision-making culminating in a set of key choices and consequences. Land system models have an important role in supporting future land use policy, but model outputs require scientific interpretation rather than being presented as predictions. The future of land system science is strongly dependent on the research community's capacity to bring together the elements of research discussed in the paper, via empirical data collection and analysis of observed processes, computer simulation across scale levels and futures analysis of alternative, normative visions through stakeholder engagement.

Effective moduli of multi-scale composites

The effective moduli of a multi-scale composite are evaluated by a bottom-up (hierarchical) modeling approach. We focus on a two-scale structure in which the small scale includes a platelet array inside a matrix, and the large scale contains fibers inside a composite matrix. We demonstrate that the principal moduli of the multi-scale composite can be fine-tuned by the platelet arrangement and orientation. As a case study, we consider the phenomenon of fiber micro-buckling within the multi-scale composite. It is found that the compressive micro-buckling strength can be considerably increased for specific platelet orientations. The multi-scale design approach presented here can be used to generate novel families of composite materials with tunable mechanical properties.

Diurnal and weekly variation of anthropogenic heat emissions in a tropical city, Singapore

The present study estimates the temporal variability of the anthropogenic heat flux density (QF) for three common land use types found in Singapore between October 2008 and March 2009. QF is estimated by separately considering the major sources of waste heat in urban environments, which are heat release from vehicular traffic, buildings and human metabolism, respectively. The individual components of QF are calculated by using a combination of top-down and bottom-up modelling approaches of energy consumption applied to the local context. Results show that over a 24-h period, magnitudes of mean hourly QF reach maximum values of 113 W m−2 in the commercial, 17 W m−2 in the high-density public housing and 13 W m−2 in the low-density residential areas, respectively. Buildings are found to be the major source of anthropogenic heat (primarily related to space cooling) in each study area, contributing to between 49–82% of QF on weekdays and 46–81% on weekends. The spatial and temporal variations of QF are attributed to differences in traffic volume, building energy consumption and population density. This is one of the first anthropogenic heat studies carried out in a tropical city and the results show that QF can be substantial and of the same order of magnitude as calculated for city centres in mid-latitude cities during winter time.

Hierarchical 3D multiphysics modelling in the design and optimisation of SOFC system components

This paper presents a novel bottom-up modelling approach to aid in the design and optimisation of the Research Centre Jülich type integrated module components. The approach is demonstrated by employing the air pre-heater component. A feasibility study considering the thermo fluid, thermo mechanical behaviour of the physical air pre-heater design compared to a simplified design is introduced. Analogue design simplifications are performed to the afterburner and pre-reformer components. The results reveal that a simplified design can be feasible for thermo fluid flow analyses, but are not representative for mechanical analyses. The integrated module considering the simplified system components is simulated in 3D, considering the multiphysics that occur within each component. The predictions of the air pre-heater component obtained from the integrated module analysis are compared to the stand-alone air pre-heater simulation results. The results are in very good agreement. The approach is proven to be useful for the optimisation of the integrated module. Moreover, the investigation of local processes, critical regions can be visualised. The methodology demonstrates the effective simulation of large scale systems in 3D.