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Forest managers working in dry forest ecosystems must contend with the costs and benefits of fire, and they are seeking forest management strategies that enhance the resilience of forests and landscapes to future disturbances in a changing climate. An interdisciplinary science team worked with resource managers and stakeholders to assess future forest ecosystem dynamics, given potential climatic changes and management strategies, across a 23,000-ha landscape in the Lake Tahoe basin of California and Nevada in support of the Lake Tahoe West Restoration Partnership. We projected forest growth and fire dynamics using a landscape change model, upon which the science team layered additional modeling to evaluate changes in wildlife habitat, water, and economics. Managers and stakeholders used the findings of this integrated modeling effort to inform the design of a landscape restoration strategy that balanced risks and benefits based on a robust scientific foundation. The results, published in this Special Feature, suggest that a continuation of status quo management would be less effective at protecting and improving desired outcomes than more active and extensive management approaches. In addition, the types of management activity also affected ecosystem outcomes. Results from across the studies in this special feature suggest that thinning and prescribed fire were complementary, although they resulted in somewhat different effects, and that low-severity use of fire had the greatest array and magnitude of ecosystem benefits. A notable exception was carbon storage, which declined with more active management and prescribed fire in particular. We highlight key findings from this Special Feature and summarize key challenges and some lessons learned in our experience of co-producing science. In short, science-management partnerships require cooperation, patience, and skill, but they are effective in increasing the capacity of land managers to navigate in an environment of rapid change and increasing uncertainty. [ABSTRACT FROM AUTHOR]

A well-developed mental model is crucial for effectively studying physiology core concepts. However, mental models can be difficult for students to represent and for instructors to evaluate and correct. Systems modeling as a visualization cognitive tool may facilitate mental model development. On the other hand, evidence of mental model development may also be represented verbally, in writing, and therefore, be evaluated. In this study, analysis of writing prompt completions illustrated progress in physician assistant student mental model formation of physiology core concepts, such as homeostasis and cell-cell communication, over time. Two cohorts of physician assistant students were invited to voluntarily submit completions of writing prompts five times over 16 months. Sessions included submissions pre- and post-small group systems modeling participation. Word frequency and word association cluster dendrogram analyses were conducted on submissions using the tm text mining package in R to provide insight into progressive changes in core concepts of word use and associations. Students demonstrated expanded core concepts systems thinking over time. This was apparent through the increased use of systems process terms, such as homeostasis, in submissions immediately following systems modeling activities. Students also increasingly included terms and associations emphasizing cell-cell communication and systems integration. The inclusion of these concepts within student mental models was demonstrably enhanced by participation in systems modeling activities. NEW & NOTEWORTHY This study applies text mining, an artificial intelligence form of natural language processing, to evaluate a series of physiology student-written prompt completions. Text mining of student writing in physiology has not yet been reported in the literature. Through the application of this technique, longitudinal trends in student development of mental models of core concepts were identified and visualized through word frequency distributions and cluster dendrograms. [ABSTRACT FROM AUTHOR]

Wearable technologies have aided in reducing pathological tremor symptoms through non-intrusive solutions that aim to identify patterns in involuntary movements and suppress them using actuators positioned at specific joints. However, during the development of these devices, tests were primarily conducted on patients due to the difficulty of faithfully simulating tremors using simulation equipment. Based on studies characterizing tremors in Parkinson's disease, the development of a robotic manipulator based on the Stewart platform was initiated, with the goal of satisfactorily simulating resting tremor movements in the hands. In this work, a simulator was implemented in a computational environment using the multibody dynamics method. The platform structure was designed in a virtual environment using SOLIDWORKS® v2017 software and later exported to Matlab® R17a software using the Simulink environment and Simscape multibody library. The workspace was evaluated, and the Kalman filter was used to merge acceleration and angular velocity data and convert them into data related to the inclination and rotation of real patients' wrists, which were subsequently executed in the simulator. The results show a high correlation and low dispersion between real and simulated signals, demonstrating that the simulated mechanism has the capacity to represent Parkinson's disease resting tremors in all wrist movements. The system could contribute to conducting tremor tests in suppression devices without the need for the presence of the patient and aid in comparing suppression techniques, benefiting the development of new wearable devices. [ABSTRACT FROM AUTHOR]

This paper presents a queuing system model that incorporates multiple priorities, multiple abandonments, and heterogeneous servers. Waiting for service easily leads to impatient behaviors. The impact of two kinds of impatient behaviors, balking and reneging, on queueing system performance is examined. The problem is formulated as continuous-time Markov chains. It also introduces a special state called the non-sojourn state to record the number of customers who abandon the system. The state transition rate matrix is transformed into a block tridiagonal matrix by appropriately setting the state numbers. A novel indicator called interstate transition frequency is proposed, which aids in distinguishing state transitions during the system evaluation process. Based on the interstate transition frequency, a set of indicators is derived to offer additional analytical perspectives for the queuing system. Finally, the proposed model is applied to an automobile repair shop to validate its effectiveness in practical scenarios. [ABSTRACT FROM AUTHOR]

One hundred years ago, Paul Weiss and Ludwig von Bertalanffy independently proposed that living organisms interact with their environment through systems. In the century that has followed, systems thinking and modeling have grown in tandem with discovery of the vast complexity of the universe at microscopic through astronomic levels. As our knowledge base increases, methods of organization for efficient mental models and insight into key variables for leverage points become crucial. This is particularly important in learning design environments. As educators increasingly grapple with fostering learning in ill-structured problem spaces, systems thinking and modeling provide means of aiding our cognitive processing capacity, enabling us to understand how to solve the problem systemically. The explosion of computing power throughout the century supports systems thinking for learning design through progressively robust modeling tools. Today, emergence of increasingly sophisticated artificial intelligence capabilities is poised to provide an expanded toolset for insights into network interconnections and leverage points. This allows for improved learning design encompassing complex world issues, such as climate change, food insecurity, health care and education. [ABSTRACT FROM AUTHOR]

Raw materials costs form an increasingly significant proportion of the total costs of renewable energy technologies that must be adopted at unprecedented rates to combat climate change. As the affordable deployment of these technologies grows vulnerable to materials price changes, effective strategies must be identified to mitigate the risk of higher input costs faced by manufacturers. To better understand potential threats to deployment, a market modeling approach was developed to quantify economic risk factors including material demand, substitutability, recycling, mining productivity, resource quality, and discovery. Results demonstrate that price changes are determined by interactions between demand growth, mining productivity, and resource quality. In the worst cases with high demand and low productivity, development of material substitutes and large recycling rates help reduce the prevalence of price risk from over 90% to under 10%. Investing in these strategies yields significant benefits for manufacturers and governments concerned about costs of materials critical to decarbonization and other advanced technologies. [ABSTRACT FROM AUTHOR]

Quantitative systems pharmacology (QSP) modeling has become an increasingly popular approach impacting our understanding of disease mechanisms and helping predict patients' treatment responses to facilitate study design or development go/no-go decisions. In this paper, we highlight the notable contributions and opportunities that QSP approaches are to offer during the drug development process by sharing three examples that have facilitated internal decisions. The barriers to successful applications and the factors that facilitate the success of the modeling approach is discussed. [ABSTRACT FROM AUTHOR]

The operational environment (OE) for the United States (US) Army is becoming increasingly complex, requiring systems engineers and architects to develop new approaches for managing the variability inherent in the OEs of today and tomorrow. In response to this growing capability gap, the US Department of Defense (DoD) established the engineered resilient systems (ERS) program. A core property of ERS is broad utility—the ability of a system to “perform effectively in a wide range of operations across multiple futures despite experiencing disruptions.” This article discusses research intended to provide system designers with a model that visualizes the complex interactions between the variables of the OE in order to derive system requirements that ensure a system exhibits broad utility. Specifically, this article 1) identifies three gaps in current US DoD doctrine impacting the ability to design technical systems for broad utility, 2) proposes an integrated network model—the operational environment exchange network (OEEN)—that specifies how the variables of the OE, exogenous to the technical systems employed by the US Army soldiers, can impact the system's broad utility, and 3) employs an established requirements writing format to demonstrate how to write broad utility system requirements. By addressing these objectives, system designers can better visualize the complex, multilayered nature of the OE with the hope of designing systems that remain effective in the face of changing external conditions. This article demonstrates how the OEEN can assist system designers in the development of broad utility system requirements that mitigate the disruptions the system might face, ensuring the system remains effective, despite changing conditions. [ABSTRACT FROM AUTHOR]

While granular computing has experienced rapid growth in the past decades and some milestones have been reached, a comprehensive study of the representation capabilities delivered by numeric prototypes and granular prototypes produced by different techniques still calls for comprehensive research and a comparative analysis. Well-constructed information granules are reflective of the nature of the numeric evidence and serve as backbones of granular classifiers and granular models. The objective of this study is to review a number of clustering paradigms aimed at the construction of information granules, discuss the development of granular prototypes, and conduct a comprehensive evaluation of quality of numeric prototypes and their corresponding augmentations coming in the form of granular prototypes. We have been witnessing many studies devoted to the construction of information granules, but a comparative analysis of the quality of information granules constructed on a basis of prototypes produced by different clustering algorithms is still lacking. In this regard, the review of the clustering algorithms supporting the formation of information granules and the comprehensive comparative study of their usefulness in classification and modeling tasks offered in this study make sense. This will promote the usage of information granules in various future works, especially classification problem and system modeling. [ABSTRACT FROM AUTHOR]

Community management is a common approach to securing access to clean water for rural residents. Its failure to sustain water services over acceptable lifetimes has been well-documented, adversely impacting human health when users return to unimproved sources during system downtimes. The objective of this study is to identify effective strategies for improving the performance of community-managed systems. The quantitative system dynamics models were developed using monitoring data for Bolivia and Colombia to simulate typical performance as infrastructure ages and to test strategies to improve performance. The two models captured the performance behavior revealed from monitoring data and were driven by the same structure: two reinforcing loops and two balancing loops. Although a strategy to professionalize the service provider enabled them to meet or exceed the target performance threshold, external financial support alone was ineffective. The paradigm shift from fix-on-failure corrective maintenance to preventive maintenance enables systems to sustain high performance levels over time. The models are useful for understanding the underlying system structure driving performance of community-managed systems and designing high-impact interventions using model simulations. [ABSTRACT FROM AUTHOR]

Background: Increasing attention is being directed at the environmental, social, and economic sustainability of the global food system. However, a key aspect of a sustainable food system should be its ability to deliver nutrition to the global population. Quantifying nutrient adequacy with current tools is challenging.Objective: To produce a computational model illustrating the nutrient adequacy of current and proposed global food systems.Methods: The DELTA Model was constructed using global food commodity balance sheet data, alongside demographic and nutrient requirement data from UN and European Food Safety Authority sources. It also includes nutrient bioavailability considerations for protein, the indispensable amino acids, iron, and zinc, sourced from scientific literature.Results: The DELTA Model calculates global per capita nutrient availability under conditions of equal distribution and identifies areas of nutrient deficiency for various food system scenarios. Modeling the 2018 global food system showed that it supplied insufficient calcium (64% of demographically weighted target intake) and vitamin E (69%), despite supplying sufficient macronutrients. Several future scenarios were modeled, including variations in waste; scaling up current food production for the 2030 global population; plant-based food production systems; and removing sugar crops from the global food system. Each of these scenarios fell short of meeting requirements for multiple nutrients. These results emphasize the need for a balanced approach in the design of future food systems.Conclusions: Nutrient adequacy must be at the forefront of the sustainable food system debate. The DELTA Model was designed for both experts and nonexperts to inform this debate as to what may be possible, practical, and optimal for our food system. The model results strongly suggest that both plant and animal foods are necessary to achieve global nutrition. The model is freely available for public use so that anyone can explore current and simulated global food systems. [ABSTRACT FROM AUTHOR]

Safe and effective exploitation of lithium ion (Li-ion) batteries requires advanced battery management systems. This article proposes a computationally efficient, control-oriented model of a Li-ion cell. The model describes the spatial nature of both the chemical species and temperature dynamics in a computationally efficient way. The method takes advantage of the algebraic structure that arises from the distributed nature of the model. We show that, by discretizing the model partial differential equations with a finite difference method, the coupling equations take a semiseparable structure for which an efficient algebra exists. This approach yields an efficient modeling tool that can be employed to design model-based estimation and control algorithms. The proposed model is validated against a high order computational fluid dynamics model showing accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Workflow management is implemented in manufacturing at many levels. The nature of processes varies at each level, hindering the use of a standard modeling or implementation solution. The creation of a flexible workflow management framework that overarches the heterogeneous business process levels is challenging. Still, one of the promises of the Industry 4.0 initiative is precisely this: to provide easy-to-use models and solutions that enable efficient execution of enterprise targets. By addressing this challenge, this article proposes a workflow execution model that integrates information and control flows of these levels while keeping their hierarchy. The overall model builds on the Business Process Model and Notation (BPMN) for modeling at the enterprise level and recipe modeling based on colored Petri net (CPN) at the production level. Models produced with both alternatives are implemented and executed in a framework supported by an Enterprise Service Bus (ESB). Loosely coupled, late-bound system elements are connected through the Arrowhead framework, which is built upon the Service-Oriented Architecture (SOA) concept. To prove its feasibility, this article presents the practical application of the model via an automotive production scenario. Note to Practitioners—The methodology detailed in this article can serve as a basis for experts who are dealing with industrial workflows. Reacting to the requirements of Industry 4.0, i.e., the virtualization, decentralization, modularity, real-time capability, and service orientation, this article provides a concept that can answer all the defined criteria. First, it adopts a new two-level approach to workflow management, which makes the understanding and control of workflows easier, enhancing transparency. Furthermore, it demonstrates how—even completely different—applications and modeling languages can be integrated into a Service-Oriented Architecture (SOA). The presented composition and the used tools are all tried and tested. Behind the solution described in this article, there is a genuine, working code wherewith the presented end-to-end workflow management can be achieved. Following the methodology detailed in this article, the readers can construct their workflow management composition. In order to report on the performance of the created solution, this article presents different measurement compositions that allow the investigation of the essential components separately, demonstrating the scalability and temporal parameters. [ABSTRACT FROM AUTHOR]

Destructive single-event effects (SEEs) pose serious challenges for the reliable use of commercial off-the-shelf (COTS) devices in space systems. We modeled a simplified, redundant system to determine single-event latchup (SEL) rates that would likely compromise system reliability, resilience, and capabilities. We then assembled a representative data set of COTS Complementary Metal Oxide Semiconductor (CMOS) parts susceptible to SELs. After investigations of these parts revealed no compact distributions or other relationships useful for $a$ priori estimation of SEL rates and showed minimal correlation between Weibull fit parameters for the cross section versus linear energy transfer (LET) data, we used nonparametric statistical techniques to generate quantile plots that can be used to bound part SEL rates for a given confidence. We used these distributions to assess the threat posed to redundant systems by destructive SEEs. [ABSTRACT FROM AUTHOR]

Increased recycling and reuse rates are a central part of the objectives laid out by the COP21. Nonetheless, the practical implementation of what has been called the circular economy, as well as its true potential, are not easily established. This is because the impact and implementation time scales of any intervention depend on knowing the lifetime of products, which is frequently unknown. This is particularly true in construction, responsible for 39% of worldwide emissions, 11% of which are embodied. Most material flow analysis (MFA) models will simply assume a range of plausible life expectancies when bottom‐up data are lacking. In this work, we propose a novel method of identification using the high quality but highly aggregated trade data available and use it to establish a "mortality curve" for buildings and other long‐lasting products. This identification method is intended to provide more reliable inputs to existing MFA models. It is widely applicable because of the general availability of the underlying data. Using it on United Kingdom trade data, we identify product classes at 1 year for packaging/home scrap, 1 to around 10 years for vehicles/equipment, and around 50 years for construction. The identification approach was then validated by using classical approaches using bottom‐up data for vehicles. [ABSTRACT FROM AUTHOR]

The electromagnetic launch system model provides a useful reference for the launch process and performance. Inductance gradient and resistance gradient of the rail, which reflect the equivalent load characteristics of the launcher, are related to shape, material of the rail, and other factors, making it difficult to characterize in the circuit model. Therefore, a method combining equivalent network with finite element analysis is proposed to solve this problem. A segmentation-based real-time trigger strategy is applied to the 4 MJ pulse power supply (PPS) system consisted of 28 modules to shoot the 0.15 kg armature on a 6 m long rail. The width, height of the rail, or separation between two rails increase individually from 20 to 180 mm. After computing the resistance and inductance gradients of the specific rail in ANSYS, the rapid system-level simulation is carried out in MATLAB-Simulink to deliver the railgun’s performance. The results show that both the resistance gradient and the inductance gradient contribute to the launch efficiency. In order to obtain a larger inductance gradient, the width of the two rails should be narrower, the height should be shorter, and the separation between the two rails should be farther. Nevertheless, in the perspective of decreasing resistance gradient, the rail should be wider, taller, and farther. Although the resistance gradient increases with the decrease of height and width, the promotion in the inductance gradient has much more significant impacts on the electromagnetic force under the same geometric change. Therefore, for the sake of a higher launching efficiency, the rails should be narrower, shorter, and farther. [ABSTRACT FROM AUTHOR]

In recent years, analytical tools and approaches to model the costs and benefits of energy storage have proliferated in parallel with the rapid growth in the energy storage market. Some analytical tools focus on the technologies themselves, with methods for projecting future energy storage technology costs and different cost metrics used to compare storage system designs. Other tools focus on the integration of storage into larger energy systems, including how to economically operate energy storage, estimate the air pollution and greenhouse gas emissions effects of storage, or understand how policy and market rules influence storage deployment and operation. Given the confluence of evolving technologies, policies, and systems, we highlight some key challenges for future energy storage models, including the use of imperfect information to make dispatch decisions for energy-limited storage technologies and estimating how different market structures will impact the deployment of additional energy storage. [ABSTRACT FROM AUTHOR]

Current overshoot in the charging is an undesired behavior that accelerates the aging of both chargers and batteries. This phenomenon is even more severe in parallel-connected multicharger systems of supercapacitor trams, as the overshooting of each charger is stacked up in the charging current. To address this issue, in this article, we propose a current-overshoot-suppression charging method for supercapacitor trams. First, the system modeling of the charging system is developed and the motivation of this article is highlighted. Then, a cooperative charging protocol is designed to achieve the current balancing of chargers, and a set point modulation method is presented to adjust the set-point of chargers smoothly with the aim of suppressing the current overshoot. The implementation of the proposed method is illustrated with a physical case study. A laboratory testbed has been built to verify the effectiveness of the design. Experiment results show that the proposed method can suppress the current overshoot without prolonging the settling time. [ABSTRACT FROM AUTHOR]

The three-phase dual active bridge (3p-DAB) converter is widely considered in dc-grid applications. Because of the higher number of switches in the 3p-DAB, it can be argued that the reliability of the 3p-DAB is reduced when compared to other isolated-bidirectional dc–dc converter topologies. The previous work has shown that the 3p-DAB can be operated in a frozen leg fault-tolerant mode, i.e., with the two transistors of the same phase being opened by their gate driver internal protections. Because the free-wheeling diodes are left self-commutated, the analytical characterization of the converter for all voltage and loading conditions is not trivial. In this article, it is proposed to open the faulty phase such as it eliminates the interaction with the faulty-phase free-wheeling diodes. This allows the converter to fall in a characterizable operating mode for all voltage and loading conditions. The results further show that the open-phase operation provides advantages over the frozen leg operation in terms of current stress and power transfer capability. Experimental results on a small-scale closed-loop gallium nitride-based prototype as well as time-domain simulation results are provided to support the theoretical analyses. [ABSTRACT FROM AUTHOR]

System-level electrostatic discharge (ESD) robustness design according to ISO10605 or IEC 61000-4-2 on printed circuit board (PCB) level is still a challenge). The blocking point is often the lack of accurate models of complete integrated circuits (ICs). Previously published methodologies to generate ESD models for ICs are all based on measurements of the complete IC, showing the drawback of complex, difficult and time-consuming measurements. This paper proposes a novel methodology to generate ESD models for complete ICs out of their design data. The proposed approach requires no measurements with the complete IC and hence can reduce the effort significantly. Furthermore, the methodology offers the possibility for automation, which further reduces the number of needed resources. It is based on the approach of black box modeling. This means simplified behavioral models are used, which contain solely the characteristic behavior in case of disturbance pulses. The methodology focuses on the transient behavior as well as the physical destruction limits of smart power products. Behavioral model in this context means the transient response due to disturbance pulse in terms of voltage drops and current drain. The procedure is explained and verified step by step on an exemplary product. It can be easily transferred to other products. System designers receive a powerful model for targeting certain robustness of their system against transient disturbances. Further enhancements of the methodology can enable also the soft failure assessment by simulation. [ABSTRACT FROM AUTHOR]

For low energy consumption and operational stability, multimode equivalent energy consumption, an optimal energy management and control method, is proposed in this paper. This method effectively solves the problem of energy distribution between the primary power—a high-power fuel cell and an auxiliary power—and a supercapacitor, in complex operating conditions. In all four states of the hybrid electric tram, i.e., traction, coasting, braking, and station parking, the optimal energy distribution method, with the highest energy efficiency of the tram, is obtained through the whole system modeling and the equivalent energy consumption calculation. In order to verify the responding speed, this method gets the analytical solution and is tested on the RT-LAB hardware-in-the-loop simulation platform. The results show that compared to the power following method the new method can reduce the overall tram energy consumption by 2.5% in complex operating conditions. What is more, when this method is successfully applied to the world's first commercial fuel cell/supercapacitor LF-LRV hybrid tram, its energy consumption and fuel cell system (FCS) output fluctuation rate are effectively reduced according to the operational data, which indicates a good chance of extending the life of the FCS as well. [ABSTRACT FROM AUTHOR]

Abstract Current practices to identify, organize, analyze, and serve data to water resources systems models are typically model and dataset-specific. Data are stored in different formats, described with different vocabularies, and require manual, model-specific, and time-intensive manipulations to find, organize, compare, and then serve to models. This paper presents the Water Management Data Model (WaMDaM) implemented in a relational database. WaMDaM uses contextual metadata, controlled vocabularies, and supporting software tools to organize and store water management data from multiple sources and models and allow users to more easily interact with its database. Five use cases use thirteen datasets and models focused in the Bear River Watershed, United States to show how a user can identify, compare, and choose from multiple types of data, networks, and scenario elements then serve data to models. The database design is flexible and scalable to accommodate new datasets, models, and associated components, attributes, scenarios, and metadata. Graphical abstract Image 1 Highlights • We present a data model to organize water resources systems data and models. • Controlled vocabularies link native terms across different datasets and models. • Software tools manage controlled vocabularies and help load datasets. • Modelers can identify and compare available data then serve data to models. [ABSTRACT FROM AUTHOR]

Acetaminophen (APAP)-induced liver injury is clinically significant, and APAP overdose in mice often serves as a model for drug-induced liver injury in humans. By specifying that APAP metabolism, reactive metabolite formation, glutathione depletion, and mitigation of mitochondrial damage within individual hepatocytes are functions of intralobular location, an earlier virtual model mechanism provided the first concrete multiattribute explanation for how and why early necrosis occurs close to the central vein (CV). However, two characteristic features could not be simulated consistently: necrosis occurring first adjacent to the CV, and subsequent necrosis occurring primarily adjacent to hepatocytes that have already initiated necrosis. We sought parsimonious model mechanism enhancements that would manage spatiotemporal heterogeneity sufficiently to enable meeting two new target attributes and conducted virtual experiments to explore different ideas for model mechanism improvement at intrahepatocyte and multihepatocyte levels. For the latter, evidence supports intercellular communication via exosomes, gap junctions, and connexin hemichannels playing essential roles in the toxic effects of chemicals, including facilitating or counteracting cell death processes. Logic requiring hepatocytes to obtain current information about whether downstream and lateral neighbors have triggered necrosis enabled virtual hepatocytes to achieve both new target attributes. A virtual hepatocyte that is glutathione-depleted uses that information to determine if it will initiate necrosis. When a less-stressed hepatocyte is flanked by at least two neighbors that have triggered necrosis, it too will initiate necrosis. We hypothesize that the resulting intercellular communication-enabled model mechanism is analogous to the actual explanation for APAP-induced hepatotoxicity at comparable levels of granularity. [ABSTRACT FROM AUTHOR]

Summary: Modern society depends on the use of many diverse materials. Effectively managing these materials is becoming increasingly important and complex, from the analysis of supply chains, to quantifying their environmental impacts, to understanding future resource availability. Material stocks and flows data enable such analyses, but currently exist mainly as discrete packages, with highly varied type, scope, and structure. These factors constitute a powerful barrier to holistic integration and thus universal analysis of existing and yet to be published material stocks and flows data. We present the Unified Materials Information System (UMIS) to overcome this barrier by enabling material stocks and flows data to be comprehensively integrated across space, time, materials, and data type independent of their disaggregation, without loss of information, and avoiding double counting. UMIS can therefore be applied to structure diverse material stocks and flows data and their metadata across material systems analysis methods such as material flow analysis (MFA), input‐output analysis, and life cycle assessment. UMIS uniquely labels and visualizes processes and flows in UMIS diagrams; therefore, material stocks and flows data visualized in UMIS diagrams can be individually referenced in databases and computational models. Applications of UMIS to restructure existing material stocks and flows data represented by block flow diagrams, system dynamics diagrams, Sankey diagrams, matrices, and derived using the economy‐wide MFA classification system are presented to exemplify use. UMIS advances the capabilities with which complex quantitative material systems analysis, archiving, and computation of material stocks and flows data can be performed. [ABSTRACT FROM AUTHOR]

Modern system-on-chip (SoC) designs comprise programmable cores, application-specific accelerators, and I/O devices. Accelerators are controlled by software/firmware and functionality is implemented by this combination of programmable cores, firmware, and accelerators. Verification of such SoCs is challenging, especially for system-level properties maintained by a combination of firmware and hardware. Attempting to formally verify the full SoC design with both firmware and hardware is not scalable, while separate verification can miss bugs. A general technique for scalable system-level verification is to construct an abstraction of SoC hardware and verify firmware/software using it. There are two challenges in applying this technique in practice. Constructing the abstraction to capture required details and interactions is error-prone and time-consuming. The second is ensuring abstraction correctness so that properties proven with it are valid. This paper introduces a methodology for SoC design and verification based on the synthesis of instruction-level abstractions (ILAs). The ILA is an abstraction of SoC hardware which models updates to firmware-visible state at the granularity of instructions. For hardware accelerators, the ILA is analogous to the instruction-set architecture definition for programmable processors and enables scalable verification of firmware interacting with hardware accelerators. To alleviate the disadvantages of manual construction of abstractions, we introduce two algorithms for synthesis of ILAs from partial description called templates. We then show how the ILA can be verified to be correct. We evaluate the methodology using a small SoC design consisting of the 8051 microcontroller and two cryptographic accelerators. The methodology uncovered 15 bugs. [ABSTRACT FROM AUTHOR]

Contribution: This paper illustrates how to design and implement curricula in terms of the combined use of flipped learning and inquiry-based learning in an engineering course. Background: Elementary courses in engineering schools are conventional and foundational, and involve a considerable amount of knowledge. Throughout such courses, students are also expected to develop insight, which cannot be obtained by only listening to instructors. Having relevant discussions is also difficult for most instructors. Intended Outcomes: The combined use of flipped learning and inquiry-based learning would be beneficial to broaden student achievement. Application Design: Based on an epistemological approach about knowledge and knowing, this paper applies the combined use of flipped learning and inquiry-based learning to enhance student knowledge and advance ways of thinking on a System Modeling and Control course. Findings: The extended learning time and the collective responsibility for learning are discussed as critical issues in applying the combined use of flipped learning and inquiry-based learning in an engineering school. [ABSTRACT FROM AUTHOR]

Objectives: In 2007, 31.7% of Georgia adolescents in grades 9-12 were overweight or obese. Understanding the impact of policies and interventions on obesity prevalence among young people can help determine statewide public health and policy strategies. This article describes a systems model, originally launched in 2008 and updated in 2014, that simulates the impact of policy interventions on the prevalence of childhood obesity in Georgia through 2034. Methods: In 2008, using information from peer-reviewed reports and quantitative estimates by experts in childhood obesity, physical activity, nutrition, and health economics and policy, a group of legislators, legislative staff members, and experts trained in systems thinking and system dynamics modeling constructed a model simulating the impact of policy interventions on the prevalence of childhood obesity in Georgia through 2034. Use of the 2008 model contributed to passage of a bill requiring annual fitness testing of schoolchildren and stricter enforcement of physical education requirements. We updated the model in 2014. Results: With no policy change, the updated model projects that the prevalence of obesity among children and adolescents aged 18 in Georgia would hold at 18% from 2014 through 2034. Mandating daily school physical education (which would reduce prevalence to 12%) and integrating moderate to vigorous physical activity into elementary classrooms (which would reduce prevalence to 10%) would have the largest projected impact. Enacting all policies simultaneously would lower the prevalence of childhood obesity from 18% to 3%. Conclusions: Systems thinking, especially with simulation models, facilitates understanding of complex health policy problems. Using a simulation model to educate legislators, educators, and health experts about the policies that have the greatest short- and long-term impact should encourage strategic investment in low-cost, high-return policies. [ABSTRACT FROM AUTHOR]

At the local scale, interconnected production, consumption, waste management, and other man-made technological components interact with local ecosystem components to form a local production system. The purpose of this work is to develop a framework for the conceptual characterization and mathematical modeling of a local production system to support the assessment of process and component options that potentially create symbiosis between industry and ecosystem. This framework has been applied to a case study to assess options for the establishment of a local energy production system that involves a heathland ecosystem, bioenergy production, and wastewater treatment. We found that the framework is useful to analyze the two-way interactions between these components in order to obtain insight into the behavior and performance of the bioenergy production system. In particular, the framework enables exploring the levels of the ecosystem states that allow continuous provisioning of resources in order to establish a sustainable techno-ecological system. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD), a neurodegenerative disorder, affects millions of people and has gained attention because of its clinical roles affecting behaviors related to motor and nonmotor symptoms. Although studies on PD from various aspects are becoming popular, few rely on predictive systems modeling approaches. Using Biochemical Systems Theory (BST), this article attempts to model and characterize dopaminergic cell death and understand pathophysiology of progression of PD. PD pathways were modeled using stochastic differential equations incorporating law of mass action, and initial concentrations for the modeled proteins were obtained from literature. Simulations suggest that dopamine levels were reduced significantly due to an increase in dopaminergic quinones and 3,4-dihydroxyphenylacetaldehyde (DOPAL) relating to imbalances compared to control during PD progression. Associating to clinically observed PD-related cell death, simulations show abnormal parkin and reactive oxygen species levels with an increase in neurofibrillary tangles. While relating molecular mechanistic roles, the BST modeling helps predicting dopaminergic cell death processes involved in the progression of PD and provides a predictive understanding of neuronal dysfunction for translational neuroscience. [ABSTRACT FROM AUTHOR]

Many approaches to engineer source strength have been proposed to enhance crop yield potential. However, a well-co-ordinated source-sink relationship is required finally to realize the promised increase in crop yield potential in the farmer's field. Source-sink interaction has been intensively studied for decades, and a vast amount of knowledge about the interaction in different crops and under different environments has been accumulated. In this review, we first introduce the basic concepts of source, sink and their interactions, then summarize current understanding of how source and sink can be manipulated through both environmental control and genetic manipulations. We show that the source-sink interaction underlies the diverse responses of crops to the same perturbations and argue that development of a molecular systems model of source-sink interaction is required towards a rational manipulation of the source-sink relationship for increased yield. We finally discuss both bottom-up and top-down routes to develop such a model and emphasize that a community effort is needed for development of this model. [ABSTRACT FROM AUTHOR]

The world's largest class hydrogen energy carrier production, storage, and utilization system has been operated in order to obtain basic data for practical use of the system using renewable energy. In this system, an alkaline water electrolyzer is combined with hydrogenation reactors to produce methylcyclohexane (MCH). Since electrolyzer behavior directly affects hydrogenation reaction, behaviors of the 150 kW class water electrolyzer against fluctuating electricity inputs were experimentally investigated. The cell stack voltage and hydrogen flow rate changed following temporal changes of the input current, whereas the temperature response was slow due to the large heat capacity of the system. Hydrogenation reactors performance using the hydrogen from the electrolyzer are reported. Then, based on the experiment data, a numerical simulation model for the electrolyzer was developed, which predicts the experimental result using fluctuating electricity very well. Furthermore, using the simulator, the heat utilization from the hydrogenation reaction for the electrolyzer warm-up process was investigated. [ABSTRACT FROM AUTHOR]

With as much as 4.8 °C increase in air temperature by end of 21st century, new crop cultivars are needed for adapting to the new climate. The objective of this study was to identify maize ( Zea mays L.) cultivar parameters that maintain yield under projected climate for late in the 21st century under full and deficit irrigation in a semi-arid region. The Root Zone Water Quality Model (RZWQM2) was calibrated with four years of maize data from northeastern Colorado, USA, under various irrigation conditions and was then used to simulate climate change effects on maize production with current management practices. Results showed that projected climate change decreased yield by 21% and biomass by 7% late in the 21st century (2070–2091) under full irrigation, compared to yield in the current climate (1992–2013). Under deficit irrigation, the corresponding reductions were 14% and 3%, respectively. Using the cultivar parameters calibrated with RZWQM2 for southern Colorado condition did not show yield decrease under future climate, but it simulated much lower yield under current climate in northeastern Colorado. A cultivar from the DSSAT (Decision Support Systems for Agrotechnology Transfer) crop database (GL 482) produced similar yield to experimental data under current climate and increased yield by 4% at full irrigation under future climate in northeastern Colorado. Using Latin Hypercube Sampling (LHS), we also identified 70 cultivars with longer maturity duration (between silking and physiological maturity) and higher grain filling rate for mitigating climate change effects on maize production. These two identified traits can guide plant breeders in developing cultivars for the future. [ABSTRACT FROM AUTHOR]

Engineering C4 photosynthesis into rice has been considered a promising strategy to increase photosynthesis and yield. A question that remains to be answered is whether expressing a C4 metabolic cycle into a C3 leaf structure and without removing the C3 background metabolism improves photosynthetic efficiency. To explore this question, we developed a 3D reaction diffusion model of bundle-sheath and connected mesophyll cells in a C3 rice leaf. Our results show that integrating a C4 metabolic pathway into rice leaves with a C3 metabolism and mesophyll structure may lead to an improved photosynthesis under current ambient CO2 concentration. We analysed a number of physiological factors that influence the CO2 uptake rate, which include the chloroplast surface area exposed to intercellular air space, bundle-sheath cell wall thickness, bundle-sheath chloroplast envelope permeability, Rubisco concentration and the energy partitioning between C3 and C4 cycles. Among these, partitioning of energy between C3 and C4 photosynthesis and the partitioning of Rubisco between mesophyll and bundle-sheath cells are decisive factors controlling photosynthetic efficiency in an engineered C3-C4 leaf. The implications of the results for the sequence of C4 evolution are also discussed. [ABSTRACT FROM AUTHOR]

Cold standby redundancy is a widely-applied design strategy to achieve high system reliability in numerous applications. To facilitate an effective system recovery in case of an online element failure occurring, the backup mechanism is typically implemented which enables a standby element to take over the mission task from a backup point instead of re-executing the entire mission task from the very beginning. However, the backup mechanism is not perfectly reliable in practice, and effect of its failure on the system reliability can be non-monotonic and is correlated to other system and element parameters. In this paper a new numerical approach is first proposed to evaluate reliability and expected mission completion time for 1-out-of-N: G cold standby systems subject to imperfect even backups. The system elements are non-repairable during the mission. Based on the proposed evaluation algorithm, effects of backup system reliability in connection with other system parameters including backup frequency, data backup and retrieval times, replacement failure probability, replacement time, and number of system elements are investigated through examples. Further, the optimal backup frequency and initiation sequencing problem is formulated and solved for heterogeneous cold standby systems, providing solutions that can maximize mission reliability or minimize expected mission completion time depending on design requirements. [ABSTRACT FROM PUBLISHER]

Novel algorithms are developed to enable the modeling of large, complex infrastructure systems as Bayesian networks (BNs). These include a compression algorithm that significantly reduces the memory storage required to construct the BN model, and an updating algorithm that performs inference on compressed matrices. These algorithms address one of the major obstacles to widespread use of BNs for system reliability assessment, namely the exponentially increasing amount of information that needs to be stored as the number of components in the system increases. The proposed compression and inference algorithms are described and applied to example systems to investigate their performance compared to that of existing algorithms. Orders of magnitude savings in memory storage requirement are demonstrated using the new algorithms, enabling BN modeling and reliability analysis of larger infrastructure systems. [ABSTRACT FROM AUTHOR]

Australia's mining sector periodically suffered huge losses both directly and indirectly from mismanagement of mine water during extreme climatic events. Mine water managers still lack cost-effective tools and strategies to manage both climate-influenced drought and flooding challenges. This paper aims to answer a fundamental question in mine water management: how much can a water sharing approach do to reduce unpermitted (unregulated) mine-affected water to overflow to the environment on a regional scale? To this end, we built a climate-driven hierarchical systems model (C-HSM) of sixteen coal mines in the Bowen Basin of Queensland, Australia. The C-HSM simulated the dynamics of the mine water systems, which was then the basis to assess the potential of mine water sharing to reduce unregulated discharge. We found that mine water sharing could greatly cut down regional unregulated discharge during the 2010–2011 wet season (which included an extreme flooding event). The cost of building such a regional-scale sharing infrastructure for redistributing water was found to be competitive to the lost revenue due to reduced coal production. The capital cost could potentially be further reduced by using existing water pipelines – such as those used for coal seam gas water management – or considering cheap transporting options. Once the sharing infrastructure has been installed, it can prevent periodic suffering of mines from climate extremes. Combined with an actively regulated discharge strategy, the water sharing approach could almost completely eliminate the unregulated discharge and maintain mine water storages at a secure level. The capital and operational cost for the combined approach can be considerably reduced and the shared water could be stored at other sites (mines, water holding ponds from coal seam gas industry) and used for water-limited periods. This is the first work that explores the theoretical potential of mine water sharing to mitigate the risk of unregulated discharge. The work presented here reveals that mine water sharing could help mine water managers respond to a sudden change in extremes such as switching from extreme ‘dry’ to extreme ‘wet’ conditions. A combined mine water sharing strategy would be worth considering by mine water managers, due to its advantages in regards to safety, relatively lower implementation costs, its effectiveness of reducing unregulated discharge that may cause environmental harm and conserving excess water for future water-limited periods. The ultimate value of water savings through a sharing scheme may be considerable as it influences mining industry's social license-to-operate in the long term, especially under conditions where there is a water security issue of decreasing availability and increasing competition. The work also highlights the benefits of systems modeling in supporting mine water managers with strategic decision making. [ABSTRACT FROM AUTHOR]

Newcache is a secure cache that can thwart cache side-channel attacks to prevent the leakage of secret information. All caches today are susceptible to cache side-channel attacks, despite software isolation of memory pages in virtual address spaces or virtual machines. These cache attacks can leak secret encryption keys or private identity keys, nullifying any protection provided by strong cryptography. Newcache uses a novel dynamic, randomized memory-to-cache mapping to thwart contention-based side-channel attacks, rather than the static mapping used by conventional set-associative caches. In this article, the authors present an improved design of Newcache, in terms of security, circuit design and simplicity. They show Newcache's security against a suite of cache side-channel attacks. They evaluate Newcache's system performance for cloud computing, smartphone, and SPEC benchmarks and find that Newcache performs as well as conventional set-associative caches, and sometimes better. They also designed a VLSI test chip with a 32-Kbyte Newcache and a 32-Kbyte, eight-way, set-associative cache and verified that the access latency, power, and area of the two caches are comparable. These results show that Newcache can be used as L1 data and instruction caches to improve security without impacting performance. [ABSTRACT FROM AUTHOR]

Cloud customers need assurances regarding the security of their virtual machines (VMs) operating within an infrastructure-as-a-service cloud system. This is complicated by the customer not knowing where the VM is executing and by the semantic gap between what the customer wants to know versus what can be measured in the cloud. In this article, the authors present an architecture for monitoring a VM's security health. Their architecture can communicate this to the customer in an unforgeable manner. The authors show a concrete implementation of property-based attestation and a full prototype based on the OpenStack open source cloud software. [ABSTRACT FROM AUTHOR]

Capability Hardware Enhanced RISC Instructions (CHERI) supplement the conventional memory management unit (MMU) with instruction-set architecture (ISA) extensions that implement a capability system model in the address space. CHERI can also underpin a hardware-software object-capability model for scalable application compartmentalization that can mitigate broader classes of attack. This article describes ISA additions to CHERI that support fast protection-domain switching, not only in terms of low cycle count, but also efficient memory sharing with mutual distrust. The authors propose ISA support for sealed capabilities, hardware-assisted checking during protection-domain switching, a lightweight capability flow-control model, and fast register clearing, while retaining the flexibility of a software-defined protection-domain transition model. They validate this approach through a full-system experimental design, including ISA extensions, a field-programmable gate array prototype (implemented in Bluespec SystemVerilog), and a software stack including an OS (based on FreeBSD), compiler (based on LLVM), software compartmentalization model, and open-source applications. [ABSTRACT FROM AUTHOR]

Information security and data privacy have steadily grown into major concerns in computing, especially given the rapid transition into the digital age for all needs--from healthcare to national defense. Among the many forms of information leakage, covert timing channels can be dangerous primarily because they involve two parties intentionally colluding to exfiltrate sensitive data by subverting the underlying system security policy. The attackers establish an illegitimate communication channel between two processes and transmit information via resource timing modulation, which does not leave any physical activity trace for later forensic analysis. Recent studies have shown the vulnerability of many popular computing environments, such as cloud computing, to these covert timing channels. With the advancements in software confinement mechanisms, shared processor hardware structures will be natural targets for malicious attackers to exploit and implement their covert-timing-based channels. In this work, the authors present a microarchitecture-level framework that detects the possible presence of covert timing channels on shared hardware. Their experiments demonstrate their ability to successfully detect different types of covert timing channels on various hardware structures and communication patterns. [ABSTRACT FROM AUTHOR]

During the past few decades, the electronics community has witnessed a growing demand for packing more and more functionalities onto a single chip. The CMOS industry has been fulfilling this demand by continually shrinking the feature sizes, now down to the 20-nm regime. However, scaling of CMOS technology beyond 20 nm is challenging because of exacerbated short-channel effects, process variations, and reliability problems. These drawbacks of CMOS technology have led to the exploration of new nanotechnologies such as nanoelectromechanical systems (NEMS) and carbon nanotube (CNT). These emerging technologies are considered promising alternatives to CMOS technology because of their potential benefits in power, performance, and reliability. However, security analysis of these emerging technologies, which is critical for current pervasive applications, remains unaddressed. In this article, the authors offer a security analysis of NEMS and CNT. They highlight the key technology-specific features of these post-CMOS technologies that can inform the design of secure systems. [ABSTRACT FROM AUTHOR]

The diffusion of palliative care has been rapid, yet uncertainty remains regarding palliative care's "active ingredients." The National Consensus Project Guidelines for Quality Palliative Care identified eight domains of palliative care. Despite these identified domains, when pressed to describe the specific maneuvers used in clinical encounters, palliative care providers acknowledge that "it's complex." The field of systems has been used to explain complexity across many different types of systems. Specifically, engineering systems develop a representation of a system that helps manage complexity to help humans better understand the system. Our goal was to develop a system model of what palliative care providers do such that the elements of the model can be described concretely and sequentially, aggregated to describe the high-level domains currently described by palliative care, and connected to the complexity described by providers and the literature. Our study design combined methodological elements from both qualitative research and systems engineering modeling. The model drew on participant observation and debriefing semi-structured interviews with interdisciplinary palliative care team members by a systems engineer. The setting was an interdisciplinary palliative care service in a US rural academic medical center. In the developed system model, we identified 59 functions provided to patients, families, non-palliative care provider(s), and palliative care provider(s). The high-level functions related to measurement, decision-making, and treatment address up to 8 states of an individual, including an overall holistic state, physical state, psychological state, spiritual state, cultural state, personal environment state, and clinical environment state. In contrast to previously described expert consensus domain-based descriptions of palliative care, this model more directly connects palliative care provider functions to emergent behaviors that may explain system-level mechanisms of action for palliative care. Thus, a systems modeling approach provides insights into the challenges surrounding the recurring question of what is in the palliative care "syringe." We described what palliative care providers do from a systems perspective. We identified 59 PC functions delivered to patients, families, and providers. Palliative care can now be described as concrete sequential processes. PC may be better described as a system, rather than behavior, level intervention. [ABSTRACT FROM AUTHOR]

Metabolic engineering and synthetic biology have enabled the use of microbial production platforms for the renewable production of many high-value natural products. Titers and yields, however, are often too low to result in commercially viable processes. Microbial co-cultures have the ability to distribute metabolic burden and allow for modular specific optimization in a way that is not possible through traditional monoculture fermentation methods. Here, we present an Escherichia coli co-culture for the efficient production of flavonoids in vivo , resulting in a 970-fold improvement in titer of flavan-3-ols over previously published monoculture production. To accomplish this improvement in titer, factors such as strain compatibility, carbon source, temperature, induction point, and inoculation ratio were initially optimized. The development of an empirical scaled-Gaussian model based on the initial optimization data was then implemented to predict the optimum point for the system. Experimental verification of the model predictions resulted in a 65% improvement in titer, to 40.7±0.1 mg/L flavan-3-ols, over the previous optimum. Overall, this study demonstrates the first application of the co-culture production of flavonoids, the most in-depth co-culture optimization to date, and the first application of empirical systems modeling for improvement of titers from a co-culture system. [ABSTRACT FROM AUTHOR]

In the field of reliability engineering and systems safety, it is a common challenge to identify the state of a system with basis in a limited set of observations of system performances. Often, there are a multitude of different possible system states, including states of damages, which compete in explaining the observations. To account for these in the context of risk-informed management of systems, the probabilities of the relevant possible different states are needed. In the present contribution, an idea on how this might be supported through big data techniques is presented. Here, systems are considered more holistically and not only as the relationship between input and output. The starting point is to establish a knowledge-consistent probabilistic representation of the system, its key performance characteristics, and the observations (exposures, condition state and performances) that may be collected from the system in reality. Monte Carlo simulations are then employed to establish the relevant scenarios of realizations of the random variables describing possible system states, system performance characteristics, and observations. Using big data classification on the simulated scenarios, the probabilities of the system being in a given state, given particular outcomes of observations, may then be straightforwardly evaluated. The application of the presented idea is illustrated through two examples considering damage identification in structural systems subject to extreme loading. • A generic framework for probabilistic systems representation, including state changes. • A novel big data-based approach to the modeling and analysis of complex systems. • Two examples from structural engineering illustrating the implementation of the above. • Numerical studies on the robustness and sensitivity of the proposed approach. [ABSTRACT FROM AUTHOR]

The U.S. Department of Energy's (DOE) Hydrogen Storage Engineering Center of Excellence (HSECoE) is developing models for hydrogen storage systems for fuel cell-based light duty vehicle applications for a variety of promising materials. These transient models simulate the performance of the storage system for comparison to the DOE's Technical Targets and a set of four drive cycles. PNNL developed models to simulate the performance and suitability of slurry-based chemical hydrogen storage materials. The storage systems of both a representative exothermic system based on ammonia borane and an endothermic system based on alane were developed and modeled in Simulink ® . Once complete, the reactor and radiator components of the model were validated with experimental data. The system design parameters were adjusted to allow the model to successfully meet a highway cycle, an aggressive cycle, a cold–start cycle, and a hot drive cycle. Finally, a sensitivity analysis was performed to identify the range of material properties where these DOE targets and drive cycles could be met. Materials with a heat of reaction >11 kJ mol −1 H 2 generated and a slurry hydrogen capacity of >11.4% will meet the on-board efficiency and gravimetric capacity targets, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Beef cattle have a significant contribution to greenhouse gas emissions globally, but they have a unique ability to digest plant material that is inedible for humans, thus producing human food from grasslands and rangelands. Additionally, many people around the world depend upon cattle ranching of grasslands and rangelands for their livelihoods. Identifying the strategies likely to have the largest impact on greenhouse gas emissions while improving or maintaining economic returns is necessary to guide future research. The goal of the current study was to evaluate four potential strategies for improving the environmental and economic sustainability of cow–calf production. The four strategies included (1) decreasing the feed required for maintenance, thus increasing the feed available for growth, (2) decreasing the time for cows to rebreed after calving, (3) increasing the digestibility of pasture grass, and (4) increasing the yield of pasture grass. A computer simulation model of a cow herd in Kansas, U.S.A., was modified to create variation in the four strategies. Decreasing the feed required for maintenance improved both environmental and economic sustainability, and increasing the yield of pasture grass improved economic sustainability, implying that these strategies should be primary targets to enhance the sustainability of cow–calf production systems. Grazing cow–calf production systems account for 60 to 70% of the greenhouse gas emissions of U.S. beef production. The objective of this analysis was to evaluate the importance of management strategies (cow maintenance energy requirements, reproductive efficiency, forage nutritive value, and forage yield) on the sustainability of cow–calf production systems using a sensitivity analysis in a production systems model. The Beef Cattle Systems Model was used to simulate a cow–calf production system in the Kansas Flint Hills using Angus genetics over a 24 year time period. The model was modified to create variation among cow herds in the base net energy for the maintenance requirement (NEm_Req), postpartum interval (PPI), grazed forage digestibility (Forage_TDN), and forage yield per hectare (Forage_Yield). The model was run for 1000 iterations/herds of a 100-cow herd. A stepwise regression analysis in conjunction with standardized regression analysis was used to identify important predictors of an indicator of greenhouse gas (GHG) emission intensity, dry matter intake per kilogram weaned, and two indicators of economic sustainability, winter feed use and returns over variable costs, using R statistical software. The most important predictor of DMI per kilogram weaned was calf weaning weight followed by NEm_Req, whereas returns over variable costs were primarily influenced by kilograms weaned per cow exposed and total purchased feed (supplement + winter feed), which were strongly influenced by NEm_Req and Forage_Yield, respectively. In conclusion, decreasing the net energy required for maintenance improved both economic and environmental sustainability, and increasing forage yield and length of the grazing season improved economic sustainability, implying that these strategies should be primary targets to enhance the sustainability of cow–calf production systems. [ABSTRACT FROM AUTHOR]

A modern and efficient information and communication technology (ICT) infrastructure is essential for managing the challenges in the complex urban systems development. The ICT infrastructure is a complex system consisting of many subsystems and interconnections, which makes the process of planning, designing, and maintaining a comprehensive ICT infrastructure expensive and difficult. Most approaches used for the ICT infrastructure modeling focus typically on a single ICT system, for example, a wireless network. This paper presents a systems modeling approach based on integrating different subsystems and their characteristics into a single model, applying system decomposition, establishing the logical relations between system components, and defining relevant key performance indicators. It is shown that this systems modeling approach facilitates holistic planning, design, and evaluation of the complex ICT infrastructure for a sustainable city. This is demonstrated in the form of a two-scenario Masdar city case study. The case study exhibits the practicality of the derived ICT model and the feasibility of the results. [ABSTRACT FROM AUTHOR]

The main objective of this work is to develop a methodology for analyzing the quality of the voltage level in the distribution power grid to identify and reduce the violations of voltage limits through the proposition of optimal points for the allocation of photovoltaic distributed generation. The methodology uses the geographic location of the power grid and its consumers to perform the grouping and classification in spatial grids of 100 × 100 m using the average annual consumption profile. The generated profiles, including the grid information, are sent to the photovoltaic distributed generation allocation algorithm, which, using an optimization process, identifies the geographic location, the required installed capacity, and the minimum number of photovoltaic generation units that must be inserted to minimize the violations of voltage limits, respecting the necessary restrictions. The entire proposal is applied in a real feeder with thousands of bars, whose model is validated with measurements carried out in the field. Different violations of voltage limits scenarios are used to validate the methodology, obtaining grids with better voltage quality after the optimized allocation of photovoltaic distributed generation. The proposal presents itself as a new tool in the work of adapting the voltage of the distribution power grid using photovoltaic distributed generation. [ABSTRACT FROM AUTHOR]

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the building of an integrated simulation tool based on a systems approach, and its contribution to local political discussion of the mitigation of microbiological contamination of the water in a coastal area. Local management schemes view water quality as a high-priority environmental objective. In practice, how far this objective is achieved depends on trade-offs between the costs of improved water treatment facilities and the acceptable impacts of water contamination. An in-the-field experiment in collaboration with local managers was carried out in the Thau lagoon on the French Mediterranean coast during the SPICOSA (Science and Policy Integration for Coastal System Assessment) project, from 2007 through 2011. It consisted of building a modeling platform and an integrated assessment framework for simulating exploratory scenarios. The modeling platform combines a dynamic contamination model, which represents the sources of microbiological contamination, wastewater treatment facilities, and physical mechanisms of lagoon contamination, with a prospective economic model, which estimates the patterns of development of economic activities in the area through a holistic approach. Exploratory scenarios are used to assess the risk of water contamination and the efficiency of management measures, under various assumptions about the evolution of the system. The contamination simulations suggest that the work currently planned by local authorities will be inadequate for preventing increased water pollution, and that additional but fairly inexpensive management measures for maintaining the current level of water quality should be considered. The integrated assessment framework estimates the ecological and socio-economic impacts of the various pollution mitigation policies in the broader context of possible local development patterns. The results illustrate how the systems approach may aid in the design of an applicable water policy based on operational objectives and feasible technical options. [Copyright &y& Elsevier]