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1. Integrated Mechanistic Engineering Models and Macroeconomic Input-Output approach to Model Physical Economy for Evaluating the Impact of transition to Circular Economy

   https://doi.org/10.1039/D1EE00544H  

   Vunnava, Venkata Sai; Singh, Shweta (January 2021, Energy & Environmental Science)

   null (Ed.)

   Sustainable transition to low carbon and zero waste economy requires a macroscopic evaluation of opportunities and impact of adopting emerging technologies in a region. However, a full assessment of current physical flows and wastes is a tedious task, thus leading to lack of comprehensive assessment before scale up and adoption of emerging technologies. Utilizing the mechanistic models developed for engineering and biological systems with macroeconomic framework of Input-Output models, we propose a novel integrated approach to fully map the physical economy, that automates the process of mapping industrial flows and wastes in a region. The approach is demonstrated by mapping the agro-based physical economy of the state of Illinois, USA by using mechanistic models for 10 sectors, which have high impact on waste generation. Each model mechanistically simulates the material transformation processes in the economic sector and provides the material flow information for mapping. The model for physical economy developed in the form of a Physical Input-Output Table (PIOT) captures the interindustry physical interactions in the region and waste flows, thus providing insights into the opportunities to implement circular economy strategies i.e., adoption of recycling technologies at large scale. In Illinois, adoption of technologies for industrial waste-water & hog manure recycling will have the highest impact by reducing > 62 % of hog industry waste, > 99 % of soybean hull waste, and > 96 % of dry corn milling (corn ethanol production) waste reduction. Small % reduction in fertilizer manufacturing waste was also observed. The physical economy model revealed that Urea sector had the highest material use of 5.52E+08 tons and green bean farming with lowest material use of 1.30E+05 tons for the year modeled (2018). The mechanistic modeling also allowed to capture elemental flows across the physical economy with Urea sector using 8.25E+07 tons of carbon per operation-year (highest) and bean farming using 3.90E+04 tons of elemental carbon per operation-year (least). The approach proposed here establishes a connection between engineering and physical economy modeling community for standardizing the mapping of physical economy that can provide insights for successfully transitioning to a low carbon and zero waste circular economy. 

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2. Water Allocation, Return Flows, and Economic Value in Water-Scarce Environments: Results from a Coupled Natural-Human System Model

   https://doi.org/10.3390/w14203280  

   Wobus, Cameron; Small, Eric; Carbone, Jared C.; Modi, Parthkumar; Kamen, Hannah; Szafranski, William; Livneh, Ben (October 2022, Water)

   In many parts of the world including the western United States, the allocation of water is governed by complex water laws that dictate who receives water, how much they receive, and when. Because these rules are generally based on the seniority of water rights, they are not necessarily focused on maximizing economic value across the entire economy. The maximization of value from water use economy-wide is a complex optimization problem that must explicitly consider each user’s water demand, willingness to pay (WTP) function, and the feedbacks among users in a coupled natural-human system model. In this study, we distill these complexities into a simple MATLAB® model developed to represent a two-user economy with water-dependent sectors representative of agriculture and industry. We feed the model with realistic values of relative water use, relative willingness to pay, and return flows to explore the relationships among these factors in water-limited systems. We find that the total economic value generated from water-dependent users depends primarily on the total water available in the system. However, for a given volume of water available, economic value is not necessarily maximized when all the water is appropriated to the user with the highest WTP. Rather, total economic value depends on the amount of water available, the relative WTP between the two users, and on the return flows generated from each sector’s water use. While our simple two-user model is a significant abstraction of the complexities inherent in natural systems, our study provides important insights into the coupled natural-human system dynamics of water allocation and use in water-limited environments. 

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3. Collaboration Requirement Planning Protocol for HUB-CI in Factories of the Future

   https://doi.org/10.1016/j.promfg.2020.01.327  

   Puwadol Oak Dusadeerungsikul, Maitreya Sreeram (January 2019, Procedia manufacturing)

   Rapid advances in production systems’ models and technology continually challenge manufacturers preparing for the factories of the future. To address the complexity issues typically coupled with the improvements, we have developed a brain-inspired model for production systems, HUBCI. It is a virtual Hub for Collaborative Intelligence, receiving human instructions from a human-computer interface; and in turn, commanding robots via ROS. The purpose of HUB-CI is to manage diverse local information and real-time signals obtained from system agents (robots, humans, and warehouse components, e.g., carts, shelves, racks) and globally update real-time assignments and schedules for those agents. With Collaborative Control Theory (CCT) we first develop the protocol for collaborative requirement planning for a HUB-CI, (CRP-H), through which we can synchronize the agents to work smoothly and execute rapidly changing tasks. This protocol is designed to answer: Which robot(s) should perform each human-assigned task, and when should this task be performed? The primary two phases of CRP-H, CRP-I (task assignment optimization) and CRP-II (agents schedule harmonization) are developed and validated for two test scenarios: a two-robot collaboration system with five tasks; and a two-robot-and-helper-robot collaboration system with 25 tasks. Simulation results indicate that under CRP-H, both operational cost and makespan of the production work are significantly reduced in the two scenarios. 

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4. Real-time Multi-resource Allocation via Structured Policy Table

   https://doi.org/10.1007/978-3-030-29035-1_36  

   Qadeer, A.; Lee, M.; Tsukamoto, K. (September 2019, The 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019))

   Mobile Edge Cloud endures limited computational resources as compared to back-end cloud. Semi-Markov Decision Process (SMDP) basedMulti-Resource Allocation (MRA) work [6] introduces optimal resource allocation for mobile requests in the resource constrained edge cloud environments. In this study, we scale existing SMDPMRA work for real-world scenarios. First, we structure the policy tables in a two dimensional matrix such that columns represent states of the system and rows for the actions. Second, we propose an index based search technique over structured policy tables. Simulation results demonstrate that our approach outperforms the legacy method and retrieves an optimal action from the policy tables in the order of microseconds, which meets the delay criteria of real-time applications in edge cloud based systems. 

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5. OKAPI: In Support of Application Correctness in Smart Home Environments

   https://doi.org/10.1109/FMEC.2019.8795349  

   Melissaris, Themis; Shaw, Kelly; Martonosi, Margaret (June 2019, Fourth International Conference on Fog and Mobile Edge Computing (FMEC))

   Typical Internet of Things (IoT) and smart home environments are composed of smart devices that are controlled and orchestrated by applications developed and run in the cloud. Correctness is important for these applications, since they control the home's physical security (i.e. door locks) and systems (i.e. HVAC). Unfortunately, many smart home applications and systems exhibit poor security characteristics and insufficient system support. Instead they force application developers to reason about a combination of complicated scenarios-asynchronous events and distributed devices. This paper demonstrates that existing cloud-based smart home platforms provide insufficient support for applications to correctly deal with concurrency and data consistency issues. These weaknesses expose platform vulnerabilities that affect system correctness and security (e.g. a smart lock erroneously unlocked). To address this, we present OKAPI, an application-level API that provides strict atomicity and event ordering. We evaluate our work using the Samsung SmartThings smart home devices, hub, and cloud infrastructure. In addition to identifying shortfalls of cloud-based smart home platforms, we propose design guidelines to make application developers oblivious of smart home platforms' consistency and concurrency intricacies. 

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