Existing Modeling Tools Research Articles

Modelling the fate of micropollutants in integrated urban wastewater systems: Extending the applicability to pharmaceuticals

Pharmaceutical active compounds (PhACs) are a category of micropollutants frequently detected across integrated urban wastewater systems. Existing modelling tools supporting the evaluation of micropollutant fate in such complex systems, such as the IUWS_MP model library (which acronym IUWS stands for Integrated Urban Wastewater System), do not consider fate processes and fractions that are typical for PhACs. This limitation was overcome by extending the existing IUWS_MP model library with new fractions (conjugated metabolites, sequestrated fraction) and processes (consumption-excretion, deconjugation). The performance of the extended library was evaluated for five PhACs (carbamazepine, ibuprofen, diclofenac, paracetamol, furosemide) in two different integrated urban wastewater systems where measurements were available. Despite data uncertainty and the simplicity of the modelling approach, chosen to minimize data requirements, model prediction uncertainty overlapped with the measurements ranges across both systems, stressing the robustness of the proposed modelling approach. Possible applications of the extended IUWS_MP model library are presented, illustrating how this tool can support urban water managers in reducing environmental impacts from PhACs discharges.

Incorporating Autonomous Vehicles in the Traditional Four-Step Model

Automated vehicles (AVs) are a concrete possibility in the near future. As AVs may shift transportation paradigms, transportation agencies are eager to update their models to consider them in planning. In this context, the use of advanced models may be challenging, given the uncertainty in the use and deployment of AVs. In this paper, we present a general framework to extend the four-step model to include AVs, and test our extension on North Central Texas Council of Governments’ model. Our approach introduces a module for AV ownership and exogenous parameters into an existing four-step model to account for changes in travel decisions for AV owners, and for the impacts of AVs on network performance. The latter is modeled using the concept of passenger-car-equivalent to avoid imposing network-wide assumptions on AVs’ capacity consumption. We analyze five scenarios, representing different assumptions on the impacts of AVs, and include references to inform the selection of modeling parameters. We compute aggregate metrics that suggest that the model is sensitive to the proposed parameters, with the passenger-car-equivalent assumptions having the largest impact on model outcomes. Results suggest that, even when we assume that AVs can better use network capacity and that trip-making rates do not drastically increase, AVs may lead to an increase of about 2.8% in vehicle-hours traveled while also improving speeds by about 1.8%. If AVs introduce additional friction on traffic, the system performance may deteriorate. The analyses presented here suggest that existing modeling tools may be adjusted to support analyses of a future with AVs.

MAINMASTseg: Automated Map Segmentation Method for Cryo-EM Density Maps with Symmetry.

For structural interpretation of cryo-electron microscopy (cryo-EM) density maps that contain multiple chains, map segmentation is an important step. If a map is segmented accurately into regions of individual protein components, the structure of each protein can be separately modeled using an existing modeling tool. Here, we developed new software, MAINMASTseg, for segmenting maps with symmetry. MAINMASTseg is an extension of the MAINMAST de novo cryo-EM protein structure modeling tool, which builds protein structures from a graph structure that captures the distribution of salient density points in the map. MAINMASTseg uses this graph and segments the map by considering symmetry corresponding density points in the graph. We tested MAINMASTseg on a data set of 38 experimentally determined EM density maps. MAINMASTseg successfully identified an individual protein unit for the majority of the maps, which was significantly better than two other popular existing methods, Segger and Phenix. The software is made freely available for academic users at http://kiharalab.org/mainmast_seg.

Development of functionalities for improved storage modelling in OSeMOSYS

Integrated systems modelling has provided insights on pathways for the sustainable use of energy, land and water resources. Among the existing modelling tools, some have been widely used to engage policy makers, due to their open source and basic structure. Yet, in the attempt of simplifying the representation of integrated systems, essential features significantly influencing the dynamics between systems may have been left aside. This study proposes an improved formulation of the Open Source Energy Modelling System (OSeMOSYS), for a better representation of energy and resource storage processes. In particular, the focus of this work is the storage losses introduction for both dams for hydropower generation and batteries for electricity storage. The modifications were applied to a case study representing key features of both a developed and developing country. The results highlight that, with low additional computational effort, a much more accurate representation of the storage technologies can be achieved. Despite the introduction of losses, renewable energy technologies tend to have a high penetration in the future energy mix thanks to storage applications that remedy their unpredictability and seasonality.

The mathematical foundations of physical systems modeling languages

Modern modeling languages for general physical systems, such as Modelica, Amesim, or Simscape, rely on Differential Algebraic Equations (DAEs), i.e., constraints of the form f(x′,x,u)=0. This drastically facilitates modeling from first principles of the physics, as well as the reuse of models. In this paper, we develop the mathematical theory needed to establish the development of compilers and tools for DAE-based physical modeling languages on solid mathematical bases.Unlike Ordinary Differential Equations (ODEs, of the form x′=g(x,u)), DAEs exhibit subtle issues because of the notion of differentiation index and related latent equations—ODEs are DAEs of index zero, for which no latent equation needs to be considered. Prior to generating execution code and calling solvers, the compilation of such languages requires a nontrivial structural analysis step that reduces the differentiation index to a level acceptable by DAE solvers.The models supported by tools of the Modelica class involve multiple modes, with mode-dependent DAE-based dynamics and state-dependent mode switching. However, multimode DAEs are much more difficult to handle than DAEs, especially because of the events of mode change. Unfortunately, the large literature devoted to the mathematical analysis of DAEs does not cover the multimode case, typically saying nothing about mode changes. This lack of foundations causes numerous difficulties to the existing modeling tools. Some models are well handled, others are not, with no clear boundary between the two classes.In this paper, we develop a comprehensive mathematical approach supporting compilation and code generation for this class of languages. Its core is the structural analysis of multimode DAE systems. As a byproduct of this structural analysis, we propose sound criteria for accepting or rejecting multimode models. Our mathematical development relies on nonstandard analysis, which allows us to cast hybrid system dynamics to discrete-time dynamics with infinitesimal step size, thus providing a uniform framework for handling both continuous dynamics and mode change events.

Basic model of information processes and behavior of a cyber defense system

Increasing the complexity and intensity of cyberattacks makes the actual task of implementing a proactive strategy for protecting technology systems (ITS). Such a strategy is associated with tight time limits on making decisions on assessing the current situation and making appropriate decisions even before the interruption of a cyber attack. These limitations significantly narrow the scope of the use of expert assessment methods. There is a growing need for the widespread use of automation tools that will significantly reduce the time for determining security events, and for formulating and implementing a decision on countering the invasion of ITS cyberspace. A feature of such tools is a significant level of intellectualization, which is adequate to the level of competence of cybersecurity operators. The basic component of the procedure for developing automation tools is the formation of a process model of the object of research. Based on this modem, using the well-known technological platforms, the corresponding software code is generated. Known means of a formalized description of business processes make it possible to form models of processes. The term “information” is widely used, but its essence is not disclosed. This, in turn, leads to a number of regarding the properties of these processes (composition, structure, functions, organization, etc.). The lack of a constructive definition of the term “information” greatly complicates the analysis of cyber defense processes. In cyberspace, security events and security are procedures for processing, storing and transmitting data (bit sets). Under these conditions, it is difficult to unambiguously express the semantic connection between the dangerous event in ITS, the objects reflecting this event, and cyber defense processes using existing modeling tools. The problem of information uncertainties in modeling a cyber defense system significantly narrows the range of processes that can be algorithmized with the aim of creating means for their automation. The solution to this problem is relevant for the sphere of automation of proactive cyber defense system processes. In order to increase the efficiency of the process of developing automation tools for modern cyber defense systems, a new model of such a system has been developed based on an attribute-transfer approach to the essence of and a basic model of processes for managing a cyber system. Within the framework of this model, the IT system is a management object, the security of which is controlled by a set of coordinated cyber protection processes. This set of processes is connected by a control cycle. The sequence of cycles is the trajectory of the cyber defense system. Corresponding graphic and mathematical models of behavior are developed. Using them, a decomposition of one of the cyber threat intelligence processes was carried out and an appropriate automation tool was developed.

The Role of Domestic Integrated Battery Energy Storage Systems for Electricity Network Performance Enhancement

Low carbon technologies are necessary to address global warming issues through electricity decabonisation, but their large-scale integration challenges the stability and security of electricity supply. Energy storage can support this transition by bringing flexibility to the grid but since it represents high capital investments, the right choices must be made in terms of the technology and the location point in the network. Most of the potential for storage is achieved when connected further from the load, and Battery Energy Storage Systems (BESS) are a strong candidate for behind-the-meter integration. This work reviews and evaluates the state-of-the-art development of BESS, analysing the benefits and barriers to a wider range of applications in the domestic sector. Existing modelling tools that are key for a better assessment of the impacts of BESS to the grid are also reviewed. It is shown that the technology exists and has potential for including Electric Vehicle battery reuse, however it is still mostly applied to optimise domestic photovoltaic electricity utilisation. The barriers to a wider integration are financial, economic, technical, as well as market and regulation. Increased field trials and robust numerical modelling should be the next step to gain investment confidence and allow BESS to reach their potential.

Existing Accessible Modeling Tools Offer Limited Support to Evaluation of Impact Investment in Rangeland Ecosystem Services

Privately owned rangelands in the western US support many ecosystem services and are threatened by financial incentives favoring conversion to housing development and more intensive forms of agriculture. Recognizing this threat, the impact investment community has identified rangeland management as a potential investing strategy to produce financial returns while preserving or enhancing the ecosystem services provided by intact rangelands. This strategy is based primarily on the notion that a capital-intensive conversion from continuous to rotational grazing can financially sustain rangelands through a combination of increased productivity and potentially monetized ecosystem service flows. The potential for these gains is supported by compelling anecdotal evidence, yet a robust body of scientific literature based on rigorous field experiments has not supported those claims, nor produced transferable estimates of the benefits provided by rotational grazing of livestock (particularly cattle in the western US). Therefore, to demonstrate investment viability and measure investment success, impact investors will likely need to address these well-documented disconnects through some combination of monitoring and process-based modeling. This study examines the extent to which existing modeling tools are up to this task, by assessing the ability of two process-based models to represent four specific rotational grazing benefits put forth by impact investors, using a ranch in northeastern Wyoming as a case study. Using the Soil Water Assessment Tool, we simulated high magnitude changes in the water balance from surface runoff to evapotranspiration, which may be a benefit or negative impact depending on context (Benefit 1). We simulated a decrease in soil water storage under rotational grazing, which directly contradicts the outcome assumed in investment literature (Benefit 2). Using the InVEST beta Rangeland Production Model (based on the Century ecosystem model), we simulated increased biomass productivity (Benefit 3) under rotational grazing, which enhanced animal performance under some management parameters but negatively impacted it in others (Benefit 4). We conclude that the impact investing community will likely find greater success through a shift to objective-oriented ranch management, rather than a specific focus on rotation, and will also need additional investment in science and monitoring to demonstrate benefits.

A Principled Approximation Framework for Optimal Control of Semi-Markov Jump Linear Systems

We consider continuous-time, finite-horizon, optimal quadratic control of semi-Markov jump linear systems (S-MJLS), and develop principled approximations through Markov-like representations for the holding-time distributions. We adopt a phase-type approximation for holding-time distributions, which is known to be consistent, and translates an S-MJLS into a specific MJLS with partially observable modes (MJLSPOM), where the modes in a cluster have the same dynamic, the same cost weighting matrices, and the same control policy. For a general MJLSPOM, we give necessary and sufficient conditions for optimal (switched) linear controllers. When specialized to our particular MJLSPOM, we additionally establish the existence of an optimal linear controller, as well as its optimality within the class of general controllers satisfying standard smoothness conditions. The known equivalence between phase-type distributions and positive linear systems allows us to leverage existing modeling tools, but possibly with large computational costs. Motivated by this, we propose a matrix-exponential approximation of holding-time distributions, resulting in pseudo -MJLSPOM representation, wherein the transition rates could be negative. Such a representation is of relatively low order, and maintains the same optimality conditions as for the MJLSPOM representation, but could violate nonnegativity of holding-time density functions. A two-step procedure consisting of a local pulling-up modification and a filtering technique is constructed to enforce nonnegativity.

OMG standard for integrating safety and reliability analysis into MBSE: Concepts and applications

AbstractModel‐Based Systems Engineering (MBSE) is gaining popularity in organizations creating complex systems where it is crucial to collaborate in a multi‐disciplinary environment. SysML, being one of the key MBSE components, has a good foundation for capturing requirements, architecture, constraints, views and viewpoints. However, SysML does not provide the necessary constructs to capture safety and reliability information in the system model. A group of industry experts at the OMG has been working since 2016 to define a new specification providing the necessary capabilities. This paper provides an update on the progress of this work. It discusses the proposed specification's use of generic concepts to allow information interchange amongst diverse analyses, its use of existing SysML constructs to provide automation of safety and reliability work in existing modelling tools, and describes several of the supported analysis methods.

Landslide Analyst—a landslide propagation model considering block size heterogeneity

Block size heterogeneity affects the propagation of landslides, particularly rock avalanches; however, it remains difficult to simulate with existing modeling tools. This paper presents a novel model that can explicitly represent block size heterogeneity in landslide propagation simulations. Monte Carlo simulation is used to compute a large number of blocks with different sizes. Two major processes based on two types of elements are applied to model the landslide propagation. The stress field of the landslide is calculated based on columns, whereas the movement is calculated based on blocks. We adopted a fluid model to establish the governing equations for the stress field and, then, applied a finite difference method to obtain the numerical solution. For the movement calculation, the model uses the motion equations of the blocks based on a Lagrangian description. Inverse Distance Weighting (IDW) interpolation is applied to reconstruct the column elements. The model was validated against experimental results from Manzella and Labiouse and the Xinmo landslide. Our simulations demonstrate that the duration and velocity of the landslides are consistent with observations, particularly that large blocks are more likely to scatter whereas small blocks tend to form a depositional fan.

Antithetic and Monte Carlo kernel estimators for partial rankings

In the modern age, rankings data are ubiquitous and they are useful for a variety of applications such as recommender systems, multi-object tracking and preference learning. However, most rankings data encountered in the real world are incomplete, which prevent the direct application of existing modelling tools for complete rankings. Our contribution is a novel way to extend kernel methods for complete rankings to partial rankings, via consistent Monte Carlo estimators for Gram matrices: matrices of kernel values between pairs of observations. We also present a novel variance-reduction scheme based on an antithetic variate construction between permutations to obtain an improved estimator for the Mallows kernel. The corresponding antithetic kernel estimator has lower variance, and we demonstrate empirically that it has a better performance in a variety of machine learning tasks. Both kernel estimators are based on extending kernel mean embeddings to the embedding of a set of full rankings consistent with an observed partial ranking. They form a computationally tractable alternative to previous approaches for partial rankings data. An overview of the existing kernels and metrics for permutations is also provided.

Distilling the role of ecosystem services in the Sustainable Development Goals

Achieving well-being for all, while protecting the environment, is one of the most pressing global challenges of our time, and a central idea in the UN Sustainable Development Goals (SDGs). We believe that integrating ecosystem services, the benefits nature provides to people, into strategies for meeting the SDGs can help achieve this. Many development goals are likely underpinned by the delivery of one or more ecosystem services. Understanding how these services could support multiple development targets will be essential for planning synergistic and cost-effective interventions. Here we present the results of an expert survey on the contributions of 16 ecosystem services to achieving SDG targets linked to environment and human well-being, and review the capacity of modelling tools to evaluate SDG-relevant ecosystem services interactions. Survey respondents judged that individual ecosystem services could make important contributions to achieving 41 targets across 12 SDGs. The provision of food and water, habitat & biodiversity maintenance, and carbon storage & sequestration were perceived to each make contributions to >14 SDG targets, suggesting cross-target interactions are likely, and may present opportunities for synergistic outcomes across multiple SDGs. Existing modelling tools are well-aligned to support SDG-relevant ecosystem service planning. Together, this work identifies entry points and tools to further analyze the role of ecosystem services to support the SDGs.

An agent-based modeling framework for cybersecurity in mobile tactical networks

Mobile tactical networks facilitate communication, coordination, and information dissemination between soldiers in the field. Their increasing use provides important benefits, yet also makes them a prime enemy target. Furthermore, their dynamic, distributed, and ad-hoc nature makes them particularly vulnerable to cyber attack. Unfortunately, most existing research on cybersecurity in mobile ad-hoc networks either uses simplistic mobility models that are easier to analyze mathematically or focuses on modeling the dynamics of civilian networks. In this work, we present an agent-based modeling framework to study malware spread in mobile tactical networks. Our framework includes military-inspired models of hierarchical command structure, unit movement, communication over short-range radio, self-propagating malware, and cyber defense mechanisms. We implement several example scenarios representing military units engaged in tactical operations on a synthetic battlefield. Finally, we conduct a case study, using agent-based simulation to analyze the impact of hierarchy and cybersecurity policies on malware spread. Our results support the claim that agent-based modeling is particularly well-suited for representing the complex organizational and spatial structures inherent to military operations, and we urge others to incorporate the key elements of our framework into existing modeling tools when performing studies of cyber attacks on mobile tactical networks and corresponding cybersecurity measures.

Inter-temporal R&D and Capital Investment Portfolios for the Electricity Industry’s Low Carbon Future

ABSTRACT A pressing question facing policy makers today in developing a long-term strategy to manage carbon emissions from the electric power sector is how to appropriately balance investment in R&D for driving innovation in emerging low- and zerocarbon technologies with investment in commercially available technologies for meeting existing energy needs. Likewise, policy makers need to determine how to allocate limited funding across multiple technologies. Unfortunately, existing modeling tools to study these questions lack a realistic representation of electric power system operations, the innovation process, or both. In this paper, we present a new modeling framework for long-term R&D and electricity generation capacity planning that combines an economic representation of endogenous non-linear technical change with a detailed representation of the power system. The model captures the complementary nature of technologies in the power sector; physical integration constraints of the system; and the opportunity to build new knowledge capital as a non-linear function of R&D and accumulated knowledge, reflective of the diminishing marginal returns to research inherent in the energy innovation process. Through a series of numerical experiments and sensitivity analyses— with and without carbon policy—we show how using frameworks that do not incorporate these features can over- or under-estimate the value of different emerging technologies, and potentially misrepresent the cost-effectiveness of R&D opportunities.

DESTINY: A Comprehensive Tool with 3D and Multi-Level Cell Memory Modeling Capability

To enable the design of large capacity memory structures, novel memory technologies such as non-volatile memory (NVM) and novel fabrication approaches, e.g., 3D stacking and multi-level cell (MLC) design have been explored. The existing modeling tools, however, cover only a few memory technologies, technology nodes and fabrication approaches. We present DESTINY, a tool for modeling 2D/3D memories designed using SRAM, resistive RAM (ReRAM), spin transfer torque RAM (STT-RAM), phase change RAM (PCM) and embedded DRAM (eDRAM) and 2D memories designed using spin orbit torque RAM (SOT-RAM), domain wall memory (DWM) and Flash memory. In addition to single-level cell (SLC) designs for all of these memories, DESTINY also supports modeling MLC designs for NVMs. We have extensively validated DESTINY against commercial and research prototypes of these memories. DESTINY is very useful for performing design-space exploration across several dimensions, such as optimizing for a target (e.g., latency, area or energy-delay product) for a given memory technology, choosing the suitable memory technology or fabrication method (i.e., 2D v/s 3D) for a given optimization target, etc. We believe that DESTINY will boost studies of next-generation memory architectures used in systems ranging from mobile devices to extreme-scale supercomputers. The latest source-code of DESTINY is available from the following git repository: https://bitbucket.org/sparshmittal/destinyv2.

MMM: A toolbox for integrative structure modeling.

Structural characterization of proteins and their complexes may require integration of restraints from various experimental techniques. MMM (Multiscale Modeling of Macromolecules) is a Matlab-based open-source modeling toolbox for this purpose with a particular emphasis on distance distribution restraints obtained from electron paramagnetic resonance experiments on spin-labelled proteins and nucleic acids and their combination with atomistic structures of domains or whole protomers, small-angle scattering data, secondary structure information, homology information, and elastic network models. MMM does not only integrate various types of restraints, but also various existing modeling tools by providing a common graphical user interface to them. The types of restraints that can support such modeling and the available model types are illustrated by recent application examples.

FALCON: a toolbox for the fast contextualization of logical networks.

MotivationMathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically.ResultsWe have developed a computational approach to contextualize logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualize networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes.Availability and implementationFALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON. FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox.Supplementary information Supplementary data are available at Bioinformatics online.

Characterizing the impact of projected changes in climate and air quality on human exposures to ozone.

The impact of climate change on human and environmental health is of critical concern. Population exposures to air pollutants both indoors and outdoors are influenced by a wide range of air quality, meteorological, behavioral, and housing-related factors, many of which are also impacted by climate change. An integrated methodology for modeling changes in human exposures to tropospheric ozone (O3) owing to potential future changes in climate and demographics was implemented by linking existing modeling tools for climate, weather, air quality, population distribution, and human exposure. Human exposure results from the Air Pollutants Exposure Model (APEX) for 12 US cities show differences in daily maximum 8-h (DM8H) exposure patterns and levels by sex, age, and city for all scenarios. When climate is held constant and population demographics are varied, minimal difference in O3 exposures is predicted even with the most extreme demographic change scenario. In contrast, when population is held constant, we see evidence of substantial changes in O3 exposure for the most extreme change in climate. Similarly, we see increases in the percentage of the population in each city with at least one O3 exposure exceedance above 60 p.p.b and 70 p.p.b thresholds for future changes in climate. For these climate and population scenarios, the impact of projected changes in climate and air quality on human exposure to O3 are much larger than the impacts of changing demographics. These results indicate the potential for future changes in O3 exposure as a result of changes in climate that could impact human health.

Electrification Planning with Focus on Hybrid Mini-grids – A Comprehensive Modelling Approach for the Global South

Electrification of rural areas is one of the major challenges in countries of the Global South. A proper electrification planning requires comprehensive electrification modelling to understand the least-cost supply options. Within this paper we apply a literature review on the requirements of comprehensive electrification modelling. This is extended by research on and testing of existing modelling tools (HOMER Energy, Network Planner, GEOSIM). This analysis reveals that still none of the existing tools can fulfill all requirements combining geospatial and detailed power system modelling especially for hybrid mini-grids. Thus we present our newly developed electrification modelling approach which overcomes identified gaps.