Modeling Metal Flow Systems

Substance flow analysis (SFA) is a frequently used industrial ecology technique for studying societal metal flows, but it is limited in its ability to inform us about future developments in metal flow patterns and how we can affect them. Equation‐based simulation modeling techniques, such as dynamic SFA and system dynamics, can usefully complement static SFA studies in this respect, but they are also restricted in several ways. The objective of this article is to demonstrate the ability of agent‐based modeling to overcome these limitations and its usefulness as a tool for studying societal metal flow systems. The body of the article summarizes the parallel implementation of two models—an agent‐based model and a system dynamics model—both addressing the following research question: What conditions foster the development of a closed‐loop flow network for metals in mobile phones? The results from in silico experimentation with these models highlight three important differences between agent‐based modeling (ABM) and equation‐based modeling (EBM) techniques. An analysis of how these differences affected the insights that could be extracted from the constructed models points to several key advantages of ABM in the study of metal flow systems. In particular, this analysis suggests that a key advantage of the ABM technique is its flexibility to enable the representation of societal metal flow systems in a more native manner. This added flexibility endows modelers with enhanced leverage to identify options for steering metal flows and opens new opportunities for using the metaphor of an ecosystem to understand metal flow systems more fully.     

Weighting in Practice:Implications for the Use of Life-Cycle Assessment in Decision Making

This article investigates how environmental trade-offs are handled in life-cycle assessment (LCA) studies in some Nordic companies. Through interviews, the use and understanding of weighting methods in decision making was studied. The analysis shows that the decision makers require methods with which to aggregate and help interpret the complex information from life-cycle inventories. They agreed that it was not their own values that should be reflected in such methods, but they were found to have different opinions concerning the value basis that should be used. The analysis also investigates the difficulties arising from using such methods. The decision makers seemed to give a broader meaning to the term weighting, and were more concerned with the comparison between environmental and other aspects than the weighting of different environmental impacts. A conclusion is that decision makers need to be more involved in modeling and interpretation. The role of the analyst should be to interpret the information needs of the decision maker, and help him or her make methodological choices that are consistent with these needs and relevant from his or her point of view. To achieve this, it is important that decision makers do not view LCA as a highly standardized calculation tool, but as a flexible process of collecting, organizing, and interpreting environmental information. Such an approach to LCA increases the chances that the results will be regarded as relevant and useful.     

Modeling and artificial intelligence approaches to enzyme systems

Modeling is a means of formulating and testing complex hypotheses. Useful modeling is now possible with biological laboratory microcomputers with which experimenters feel comfortable. Artificial intelligence (AI) is sufficiently similar to modeling that AI techniques, now becoming usable on microcomputers, are applicable to modeling. Microcomputer and AI applications to physiological system studies with multienzyme models and with kinetic models of isolated enzymes are described. Using an IBM PC microcomputer, we have been able to fit kinetic enzyme models; to extend this process to design kinetic experiments by determining the optimal conditions; and to construct an enzyme (hexokinase) kinetics data base. We have also used a PC to do most of the constructing of complex multienzyme models, initially with small simple BASIC programs; alternative methods with standard spreadsheet or data base programs have been defined. Formulating and solving differential equations in appropriate representational languages, and sensitivity analysis, are soon likely to be feasible with PCs. Much of the modeling process can be stated in terms of AI expert systems, using sets of rules for fitting and evaluating models and designing further experiments. AI techniques also permit critiquing and evaluating the data, experiments, and hypotheses being modeled, and can be extended to supervise the calculations involved.     

Entity Normalization in Life Cycle Assessment: Hybrid Schemes Applied to a Transportation Agency Case Study

Government agencies, companies, and other entities are using environmental assessments, like life cycle assessment (LCA), as an input to decision‐making processes. Communicating the esoteric results of an LCA to these decision makers can present challenges, and interpretation aids are commonly provided to increase understanding. One such method is normalizing results as a means of providing context for interpreting magnitudes of environmental impacts. Normalization is mostly carried out by relating the environmental impacts of a product (or process) under study to those of another product or a spatial reference area (e.g., the United States). This research is based on the idea that decision makers might also benefit from normalization that considers comparisons to their entity's (agency, company, organization, etc.) total impacts to provide additional meaning and aid in comprehension. Two hybrid normalization schemes have been developed, which include aspects of normalization to both spatially based and entity‐based impacts. These have been named entity‐overlaid and entity‐accentuated normalization, and the schemes allow for performance‐based planning or emphasizing environmental impact types that are most relevant to an entity's operational profile, respectively. A hypothetical case study is presented to demonstrate these schemes, which uses environmental data from a U.S. transportation agency as the basis for entity normalization factors. Results of this case study illustrate how entity‐related references may be developed, and how this additional information may enhance the presentation of LCA results using the hybrid normalization schemes.     

Applying Industrial Symbiosis to Smallholder Farms

The study of industrial symbiosis (IS) has largely focused on the exchange of energy and materials among industrial processes in an effort to increase value and reduce environmental impact. Agricultural systems, particularly those located in developing countries, can benefit from the principles of IS. Relatively few studies have analyzed the potential benefits of integrated material and energy flows in smallholder farming, even though these systems are considered essential to the world's food supply and poverty reduction. Although the concepts can be applied to virtually any system, the study of industrial symbiosis has traditionally focused on industrialized systems in developed countries. The research presented here applies the principles of IS to smallholder farms using optimization techniques to maximize farm output while minimizing wastes. Our research links IS to the growing field of integrated farming research (IFR), which seeks to create new technologies that increase the production of farms by viewing the farm as a system. Bridging these fields enriches the potential for robust research outcomes in both areas and fills a current knowledge gap. IS benefits from exploring new applications and increasing its penetration into the developing world. IFR benefits from established IS tools to create alternate pathways for increased output based on symbiotic relationships. A small farming system in Liberia, West Africa, is used as a case study. System integration of individual unit processes shows increased productivity and decreased waste. The results of this analysis indicate that there are unrealized opportunities for IS in developing countries, and integration of IS techniques into smallholder farming operations has the potential for impacting sustainable development.     

Modeling composting kinetics: A review of approaches

Composting kinetics modeling is necessary to design and operate composting facilities that comply with strict market demands and tight environmental legislation. Current composting kinetics modeling can be characterized as inductive, i.e. the data are the starting point of the modeling process and determine the type of model used. It is argued that the inductive empirical approach has been developed to its limit of practicality. Further progress is not expected because of limits in measurement techniques and the resources needed to perform all experiments needed.Contrary to the inductive, the deductive modeling approach uses the existing theory as its starting point for model development. Deductive models of realistic situations contain many basic parameters representing the theoretical basis. These basic parameters however tend to be non-identifiable, limiting practical application.To overcome this problem, it is proposed that the basic parameters in the deductive model must be combined to a smaller number of so-called combined parameter that are identifiable. In this way a model is developed that can incorporate both the theoretical knowledge introduced via the basic parameter and the information of data as represented by the identifiable combined parameters.As an example of how information of both theory and data can be used, the case of the temperature effect on the composting rate is analyzed. The temperature effect is quantified as the activation energy E, a parameter derived from the well-known Arrhenius equation. The theoretical analysis shows that the E-value changes strongly during the process, which is very remarkable, as the E value of basic parameter remains constant. These results are in accordance with literature findings. The results suggest that the multiplicative approach used in first-order modeling should be reconsidered, as both the literature findings as well as the theoretical analysis of the model predict a shift in E-value. Missing a shift in the E-value could lead for instance to instability in temperature control algorithms.     

Looking to the Future of Ecosystem Services

Ecosystem services—the benefits that people obtain from ecosystems—are essential to human existence, but demands for services often surpass the capacity of ecosystems to provide them. Lack of ecological information often precludes informed decision making about ecosystem services. The Millennium Ecosystem Assessment (MA) was conceived in part to provide the necessary ecological information to decision makers. To this end, the MA set out to address the stated needs and concerns of decision makers and examine the ecological dynamics and uncertainties underlying these concerns. To improve our understanding of their information needs and concerns, we interviewed 59 decision makers from five continents. The respondents indicated that although most people generally agree about the ideal state of the planet—free of poverty and extreme inequality, replete with cultural and biological diversity—they often disagree about the best way to achieve these goals. Further, although nonspecialists are generally concerned about the environment and may have a good understanding of some of issues, they often have a more limited grasp of the ecological dynamics that drive the issues of concern. We identify some of the principal uncertainties about ecosystem dynamics and feedbacks that underlie the concerns of decision makers. Each of the papers in this special feature addresses these ecological feedbacks from the perspective of a specific discipline, suggesting ways in which knowledge of ecological dynamics can be incorporated into the MA’s assessment and scenario-building process.     

Examples of Mathematical Modeling: Tales from the Crypt

Mathematical modeling is being increasingly recognized within the biomedical sciences as an important tool that can aid the understanding of biological systems. The heavily regulated cell renewal cycle in the colonic crypt provides a good example of how modeling can be used to find out key features of the system kinetics, and help to explain both the breakdown of homeostasis and the initiation of tumorigenesis.We use the cell population model by Johnston et al. (2007) Proc. Natl. Acad. Sci. USA 104, 4008-4013, to illustrate the power of mathematical modeling by considering two key questions about the cell population dynamics in the colonic crypt. We ask: how can a model describe both homeostasis and unregulated growth in tumorigenesis; and to which parameters in the system is the model most sensitive? In order to address these questions, we discuss what type of modeling approach is most appropriate in the crypt.We use the model to argue why tumorigenesis is observed to occur in stages with long lag phases between periods of rapid growth, and we identify the key parameters.     

How can we improve information delivery to support conservation and restoration decisions?

Incorporating science into resource conservation and management is becoming increasingly important, but it is not yet clear how to provide information to decision makers most effectively. To evaluate sources of information used to support the management and conservation of California’s riparian bird habitat, we distributed a questionnaire to restoration practitioners and public and private land managers. We asked respondents to rate the importance and availability of different sources of information they use to inform their decisions. Synthetic reviews and peer-reviewed publications both received high importance and availability ratings. Web-based tools received low importance and availability ratings. One-on-one interactions between ecologists and decision makers received high importance ratings, similar to those of peer-reviewed publications and synthetic reviews, but their availability was rated lower than any other method of decision support. Our results suggest that the decision makers we surveyed are already using a wide variety of information, but that prioritizing one-on-one interactions between scientists and decision makers will enhance the delivery of all sources of information.     

An integrative approach to understanding mechanosensation

The ability for a living organism to sense and respond to its external environment is crucial to its survival. Understanding mechanosensation, the mechanism by which organisms react in response to mechanical stimuli, presents many interesting and challenging problems for both experimental and computational biologists. A major difficulty in studying mechanosensors is their inherent multiscale nature. The systems involved in mechanosesnsing can span eight orders of magnitude in length scale and up to 10 orders of magnitude in time scale. Trying to ascertain information across these length and time scales simultaneously is challenging. This problem has led to the need to approach these types of problems using an integrative approach, combining both computational and experimental biology. This review classifies the major types of mechanosensors and explains methods that have been employed in understanding their behavior, both using modeling and experimental techniques. Multiscale modeling methods combined with experimental techniques in an integrative approach are suggested as ways of undertaking the study of such systems.     

A model management systems approach to manufacturing systems design

Manufacturing systems design involves the solution of a complex series of interrelated problems. This complexity will increase in the future as manufacturing practices change to meet increased global competition. Research within manufacturing systems design has mainly been focused on finding improved models for solving particular problems, or extending existing modeling techniques. This has resulted in numerous modeling tools being available to support manufacturing systems design. However, little research work has been carried out into consolidating the existing theories and models. As a result, a large body of this work has not been applied in industry. Model management has evolved as a research area which investigates methods for storing, modifying, and manipulating models. This article describes a prototype model management system for manufacturing systems design. The objective here is not to develop “another” decision support system for manufacturing design, but to illustrate, through the development of a prototype system, a number of key ideas of how concepts from the area of model management systems can be used to support manufacturing systems design. The prototype model management system utilizes the structured modeling framework and uses an extended version of the structured modeling language. An important aspect of the prototype model management system is the incorporation of the model development task, thus allowing the system to be easily updated and adapted. The prototype system was evaluated using a range of queueing network models for manufacturing systems design.     

Integrated Modeling Framework for Manufacturing Systems: A Unified Representation of the Physical Process and Information System

The task of process modeling in a manufacturing environment centers around controlling and improving the flow of materials. This flow comprises a complicated web of control and physical systems. Despite a variety of manufacturing system modeling approaches, more rigorous process modeling is required. This paper presents an integrated modeling framework for manufacturing systems (IMF-M). Conceptual modeling of physical materials flow supported by a graphical representation facilitates improvement of operations in manufacturing environments. A declarative and executable representation of control information systems helps to improve information management by managing a variety of information models with improved readability and reusability. A unified representation of the physical process and information system provides a common modeling milieu in which efforts can be coordinated among several groups working in the different domains of scheduling, shop floor and logistics control, and information system. Since the framework helps adapt to the changes of the physical process and information system affecting each other in a consistent manner, the modeling output enhances integration of the manufacturing system.     

Modeling FMS with decision Petri nets

Decision point extended timed Petri nets or decision Petri nets (DPN) are introduced as an extended modeling framework for FMS performance evaluation. The decision point extension allows the explicit modeling of the control of the flow of tokens in timed Petri nets and hence represents the control of the flow of material, resources, and information in FMS. Further, the concept of a bounded transition is proposed to conveniently model the blocking logic in an FMS with limited buffer capacities. The motivation to present these conventions is to develop a user-friendly graphic model to represent FMS designs for analysis by discrete event simulation. DPN affords concise models that can be conveniently developed and easily transformed into discrete event simulation models. With the help of a simple FMS example, which includes a number of part types, loading rules, dispatching rules, and probabilistic branching (at an inspection station), we illustrate the DPN model development. As an illustration of the ease with which it can be tranformed into a simulation model, we have developed a generalized simulator called ROBSIM and outline here its methodological basis. The proposed concepts should be of interest to users of discrete event simulation in FMS design or elsewhere to tap the potential of basic Petri net concepts for graphic representation and specification purposes. In particular, our work should encourage other researchers to develop extensions relevant to their own areas of interest.     

Modeling of dynamical systems through deep learning

This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.     

Modeling the lipid component of membranes

During the past several years, there have been a number of advances in the computational and theoretical modeling of lipid bilayer structural and dynamical properties. Molecular dynamics (MD) simulations have increased in length and time scales by about an order of magnitude. MD simulations continue to be applied to more complex systems, including mixed bilayers and bilayer self-assembly. A critical problem is bridging the gap between the still very small MD simulations and the time and length scales of experimental observations. Several new and promising techniques, which use atomic-level correlation and response functions from simulations as input to coarse-grained modeling, are being pursued.     

Considerations for the development of a terrestrial index of ecological integrity

Ecological systems are composed of complex biological and physical components that are difficult to understand and to measure. However, effective management actions and policy decisions require information on the status, condition, and trends of ecosystems. Multiple levels of information are needed to make effective decisions and the ideal indicators for measuring ecosystem integrity will incorporate information from multiple dimensions of the ecosystem. A terrestrial index of ecological integrity would be a useful tool for ecosystem managers and decision makers. The ideal requirements of the terrestrial index of ecosystem integrity (TIEI) are that it be comprehensive and multi-scale, grounded in natural history, relevant and helpful, able to integrate concerns from aquatic and terrestrial ecology, and that it be flexible and measurable.The objective of this research is to investigate if an index, or indices, could be developed that would summarize the condition of ecosystems so that changes can be tracked over time and this information utilized as a tool to support environmental decision making.     

Modeling nucleic acids

Nucleic acids are an important class of biological macromolecules that carry out a variety of cellular roles. For many functions, naturally occurring DNA and RNA molecules need to fold into precise three-dimensional structures. Due to their self-assembling characteristics, nucleic acids have also been widely studied in the field of nanotechnology, and a diverse range of intricate three-dimensional nanostructures have been designed and synthesized. Different physical terms such as base-pairing and stacking interactions, tertiary contacts, electrostatic interactions and entropy all affect nucleic acid folding and structure. Here we review general computational approaches developed to model nucleic acid systems. We focus on four key areas of nucleic acid modeling: molecular representation, potential energy function, degrees of freedom and sampling algorithm. Appropriate choices in each of these key areas in nucleic acid modeling can effectively combine to aid interpretation of experimental data and facilitate prediction of nucleic acid structure.