students

Hadear Hassan Wins 2024 James J. Cain ’51 Award

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October 10, 2024

BiSSL Ph.D. student Hadear Hassan has been awarded the 2024 James J. Cain ’51 Award by the department, an award that recognizes the demonstrated academic and innovative excellence of the recipients and is awarded to only 2 graduate students each year. She received the award at the 2024 Mechanical Engineering Student Award Recognition on October 10th.

Dr. Astrid Layton and Ph.D. student Hadear Hassan at the Fall 2024 MEEN Award Ceremony.

James J. Cain ’51 was a long-time supporter and graduate of the Department of Mechanical Engineering at Texas A&M University. Cain was the youngest of five children and was born and raised in Sherman, Texas. After completing high school, Cain attended Texas A&M and received a degree in mechanical engineering. During his long and distinguished career of more than 35 years at Mobil Oil, Cain was renowned for his desire to mentor students and faculty at Texas A&M. He took great pride in being a part of Mobil’s college recruiting team, often filling positions with Aggie graduates.

Journal Paper on Circular Economy in Manufacturing Published in JMSE

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Two BiSSL students, Ph.D. student Hadear Hassan and MS alumna Amira Bushagour, have coauthored a paper in ASME’s Journal of Manufacturing Science and Engineering. The paper, originally presented at the 2024 MSEC conference, focuses on useful overlaps between reconfigurable manufacturing and circular economy efforts by assessing the adaptability and connection to circular economy principles in 44 different manufacturing system configurations.

Reconfigurability in manufacturing signifies a system’s capacity to promptly adapt to evolving needs. This adaptability is critical for markets to maintain operations during unexpected disruptions, including weather anomalies, cyber-attacks, and physical obstructions. Concurrently, the concept of a circular economy is gaining popularity in manufacturing to mitigate waste and optimize resource utilization. Circular economy principles aim to reduce environmental impacts while maximizing economic benefits by emphasizing the reuse of goods and resource byproducts. The nexus between reconfigurability and the circular economy stems from their shared pursuit of sustainability and resilience. Interestingly, biological ecosystems also exhibit these traits, showcasing exceptional adaptability to disturbances alongside the ability to effectively utilize available resources during normal operations. This study explores various manufacturing system configurations to assess both their adaptability and connection to circular economy principles. 44 configurations are categorized based on layout (e.g., job shop, flow line, cellular) and analyzed using convertibility, cyclicity, and Degree of System Order metrics. A significant positive correlation (R2 = 0.655) is found between high convertibility and ecologically similar levels of structural cycling, suggesting that effective resource utilization supports adaptability in manufacturing systems. Furthermore, this paper proposes the existence of a possible “window of vitality” for cyclicity, as it demonstrates a significant correlation (R2 = 0.855) between the Degree of System Order and cyclicity. Identifying systems that strike a balance between redundancy, efficiency, convertibility, and cyclicity can aid manufacturing system designers and decision-makers in making choices that address increasing requirements for both sustainability and resilience.

Hassan, H., A. Bushagour, and A. Layton. (2024) “Resilient Circularity in Manufacturing: Synergies between Circular Economy and Reconfigurable Manufacturing.” ASME Journal of Manufacturing Science and Engineering. 146(11): 110902. DOI: 10.1115/1.4065744

2 New Cyber-Physical Power Systems Papers Published

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Two new papers have been recently published from the BiSSL group first-authored and co-authored by its students resulting from a collaborative grant with Sandia National Lab and Dr. Kate Davis’ group in Electrical Engineering.

Highlights include:

1. A graph-embedding technique, Node2Vec, to capture neighborhood relationships with second-order biased random walks for risk assessment in cyber-physical power grids.
2. Highly realistic and synthetic cyber network topologies for power grids to perform our case studies.
3. Visualization of the risk assessment with various methods for a more comprehensive situational awareness for grid operators.

Modern power grids and other complex systems are a fusion of physical and cyber components, giving rise to intricate interdependencies. These interdependencies, however, also expose vulnerabilities that can be exploited by adversaries. This paper delves into the critical examination of these interconnections, inspired by Ecological Network Analysis (ENA) techniques. By drawing from ecological modeling, we aim to understand the role of cyber-physical interdependencies in the resilience of complex systems. We introduce various modeling methods, including bipartite and tripartite networks, to analyze and map these interdependencies in the context of the IEEE WSCC 9-bus and the ACTIV 200-bus case study. The paper explores how these models can identify key actors and assess network resilience. Through a detailed methodology, we apply ecological metrics and community identification techniques to comprehensively evaluate the system’s interactions. The findings offer insights into the interplay of cyber and physical elements in power grids and other complex systems. These analysis methods show that tripartite networks produce more information on indirect interactions within a complex network. Additionally, they provide detailed information on how disturbances could propagate in a cyber-physical power system. Denial of service scenarios for the WSCC 9-bus and the ACTIV 200-bus case studies are employed to support this conclusion.

Payne, E., S. Hossain-McKenzie, N. Jacobs, K. Davis, A. Layton. (2024) “Analyzing Cyber-Physical Modularity and Interdependence Using Bio-Inspired Graph Modeling.” IEEE Access. DOI: 10.1109/ACCESS.2024.3450368
This article explores vulnerabilities in modern power systems due to interdependencies between physical, cyber-physical, and cyber devices. Ecological Network Analysis (ENA) assists in constructing bipartite and tripartite networks for the WSCC 9-bus and ACTIV 200-bus systems. The findings reveal that tripartite networks offer deeper insights into indirect interactions, enhancing our understanding of systemic resilience under denial-of-service scenarios.

Abstract: Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large-scale power infrastructures as cyber-physical systems is still in its early stages. Drawing inspiration from machine-learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large-scale cyber-physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high-dimensional structures in low-dimensional spaces, facilitating advanced machine-learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding-based approach, the authors present a structured and methodical framework for risk assessment in cyber-physical systems. The proposed graph embedding-based risk analysis framework aims to provide a more insightful perspective on cyber-physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber-physical power system models: the Western System Coordinating Council (WSCC) 9-Bus System and the Illinois 200-Bus System, thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.

Sun, S., H. Huang, E. Payne, S. Hossain-McKenzie, N. Jacobs, H. Vincent Poor, A. Layton, and K. Davis. (2024) “A Graph Embedding-Based Approach for Automatic Cyber-Physical Power System Risk Assessment to Prevent and Mitigate Threats at Scale.” The Institution of Engineering and Technology (IET) Cyber-Physical Systems: Theory & Applications. DOI: 10.1049/cps2.12097

Another Successful ASME IDETC Conference!

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This year’s ASME IDETC-CIE conference saw one BiSSL Ph.D. student, Emily Payne, presenting her first-authored paper and Dr. Layton serving as an invited panelist for 2 panels.

Emily’s paper, “Integrating Machine Learning into the Design of Green Building Systems,” was presented in the SEIKM: Systems Engineering and Complex Systems.

Abstract: Sustainable infrastructure design is a complicated process often requiring detailed estimates specifications and constraints of the project scope to be compiled. Beyond the time-consuming gathering of project data sometimes the availability of completed projects is limited. Therefore, a method to produce similar designs with varied constraints requires a systems engineering perspective. Systems engineering provides a method to evaluate multidisciplinary design development while simultaneously following stakeholder requirements. Ecologically inspired systems have shown the ability to maintain balanced resources and structural relationships even under duress. Driven by the imperative to build sustainable infrastructure, this research explores the utilization of machine learning techniques to generate robust and reliable forecasts of green building specifications, even when design resources are scarce. To demonstrate the effectiveness of this approach, machine learning techniques were performed on a dataset of 93 green educational buildings, and on an oversampled dataset containing synthetically generated data points at the aim of certification level prediction. Both datasets contained metrics quantitatively characterizing cost, energy efficiency, and ecologically sustainable metrics specific to each building. Results indicate that the oversampled dataset allowed for better machine learning prediction among the classification algorithms considered. Oversampled data provided quality information offering cost minimization during initial design stages. This data suggests that oversampling is a reliable technique to amplify the design area of infrastructure projects when applied on data containing strong systemic classification patterns.

A panel session “Approaches to Environmental Sustainability, Perspectives from Europe and North America” hosted by the Design Society and organized by Dr. Julie Linsey invited me along with Dr. Abigail Clarke-Sather from the University of Minnesota Duluth and Dr. Devarajan Ramanujan from Aarhus University. The talks and discussion highlighted recent work at the interface of engineering design and circular economy.

Session Description: Minimizing impacts on the environment through clean energy, sustainability, and similar topics continues to grow and be critical topics in engineering.  It is a topic that attracts significant interest in both North America and Europe and benefits from the diverse regional perspectives.  In this special session, leading researchers from Europe and North America will present their perspectives on current needs, research approaches, cutting-edge research, and differing viewpoints.  This session will feature 7-minute short, thought-provoking presentations followed by panelist discussions and questions.   The focus will be on bringing diverse perspectives and cutting-edge research from both communities together, thereby building greater connections between the European Design Society community and the primarily North American ASME IDETC community. 

A panel special session “Opportunities at the Boundaries between Systems Engineering and Design Theory” hosted by the Design Theory and Methodology technical committee and organized by Drs. Bryan Watson and Alex Murphy invited me along with Diarny Fernandes from the Johns Hopkins University Applied Physics Laboratory and Dr. Matthew Mueller from PTC. The discussion highlighted useful intersections and emerging problems between design theory and systems engineering.

PhD Student Hadear Hassan Visits Portland, Oregon to Attend ASEE 2024

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BiSSL Ph.D. student Hadear Hassan presented a collaborative paper in the Design in Engineering Education Division (DEED) for Student-Centered Approaches in Design Education titled “Engineering the Next Generation of Innovators: Analysis of Students’ Innovation Habits.” The paper was coauthored by BiSSL alumn Luis Rodriguez and collaborator Dr. Cynthia Hipwell.

In today’s rapidly evolving landscape, innovation is the cornerstone of high value creation. This fast pace necessitates a fundamental reevaluation of educational paradigms. The imperative to emphasize lifelong learning, inquisitiveness, innovation, and an unwavering commitment to continuous self-improvement is abundantly clear. At the heart of this educational recalibration lies two questions: To what extent can personal innovativeness be honed and amplified through curricular innovations? and What are the requisite strategies for nurturing students’ practical skills, thereby enabling a perpetual journey of discovery? This work is focused on the design of a course called “Innovation Mind and Skill Sets for Design and Research,” tailored specifically for students in the STEM disciplines. The class equips students with a comprehensive innovation-focused skill set, empowering them to synthesize their specialized knowledge within broader societal contexts and, in turn, navigate the complex terrain of breakthrough innovation. This paper delves into the course’s framework, which draws inspiration from the vast reservoir of innovation literature and two decades of the instructor’s industry experience applying and improving innovation business processes with her teams in a fast-paced, high-tech industry. The core hypothesis of this paper is that innovation is fundamentally a learning process, that personal innovativeness can be cultivated and elevated through the teaching of established principles derived from the realm of learning science. These principles encompass the elevation of metacognition, the deliberate integration of intentionality into the learning process, and the embedding of reflective practices into the students’ educational journeys. In addition, the curriculum integrates pedagogical principles related to systems thinking and Transformative Learning Theory for adults. The coursework is designed to impart practical techniques that serve as scaffolds for students’ innovation processes and enhances their metacognition. The journey through this educational framework leads to an ascent through the tiers of Bloom’s Taxonomy, guiding students to cultivate enduring habits that are essential for the sustenance of the innovation process. These practical skills are honed through active participation in a team project, revolving around the innovation process, with guidance and feedback from innovation practitioners. The learning experience is further enriched through a deliberate emphasis on reflection, integrated into classroom presentations. These aspects of student progress and improvement are assessed against traditional design curricula using the Innovator Mindset® Assessment. The results are analyzed to underscore the impact of curricular innovation in fostering and amplifying personal innovativeness.

Hadear Ibrahim Hassan, Luis Angel Rodriguez, Astrid Layton, David Christopher Seets, M. Cynthia Hipwell (2024) “Engineering the Next Generation of Innovators: Analysis of Students’ Innovation Habits.” 2024 ASEE Annual Conference & Exposition. Portland, Oregon.

BiSSL collaborator Dr. Julie Linsey was also in attendance and presented a poster in the NSF Grantees Poster Session on a paper coauthored by BiSSL MS student Pepito Thelly. “Board 358: Quantitative Network Analysis for Benchmarking and Improving Makerspaces.”

Makerspaces on university campuses have seen tremendous growth and investments in recent years. Growing empirical data demonstrates the significant learning benefits to engineering students. Makerspaces are a new tool in the engineering educators’ toolbox, and as such much more needs to be done to ensure these spaces effectively grow and meet their full potential. This grant has been developing a novel network analysis technique for makerspaces, to enable the underlying makerspace network structure to be understood in terms of its connection to the successful and impactful functioning of makerspaces. The work has uncovered some basic structural building blocks of makerspace networks, known as modules, and the tools and students that make up those modules. This network-level understanding of the space enables actions such as effectively removing previously undiscovered hurdles for students who are underutilizing spaces, guiding the design of an effective makerspace from the ground up at locations with fewer resources, and creating effective events or course components that introduce students to the space in such a way that increases their chances of returning. A deep understanding of the network structure that creates a successful makerspace also provides guidance to educators on things like the impact of adding particular learning opportunities through workshop or curriculum integration, and insight into the network-level impacts of the addition of new tools or staff. The work done over the past 3 years has tried to address the following key objectives: (1) Understand the role that network analysis can play in both understanding the connection between the structure and successful functioning of a makerspace. (2) Create design guidelines for both new makerspaces and the growth of existing makerspaces, derived from modularity analyses of two successful makerspace case studies. (3) Identify potential roadblocks that prevent students, especially underrepresented minority students, from feeling comfortable in and using makerspaces. Benefits of network analysis techniques include the ability to break down a seemingly complex and chaotic makerspace into actors interacting with tools. This data was obtained through short end-of-semester student surveys. Early work found that these end-of-semester surveys provide sufficient data for the proposed analyses and are comparable to survey information provided by students as they enter and exit the space.

Claire Kaat, Pepito Thelly, Julie Linsey, Astrid Layton (2024) “Quantitative Network Analysis for Benchmarking and Improving Makerspaces.” 2024 ASEE Annual Conference & Exposition. Portland, Oregon.

Successful ASME 2024 MSEC Manufacturing Science & Engineering Conference

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BiSSL Ph.D. student Hadear Hassan presented work at the annual ASME 2024 MSEC Manufacturing Science & Engineering Conference in Knoxville, TN. She presented two papers, both co-authored with BiSSL alum Amira Bushagour (who is now a Ph.D. student at Aarhus University in Denmark). One of the papers has already been selected for publication in the special issue of ASME’s Journal of Manufacturing Science and Engineering (JMSE).

Abstract: The circular economy (CE) is a resource system in which byproducts and traditional end-of-life resource flows are fed back into the system to reduce virgin resource use and waste production. Emerging technologies offer an exciting opportunity to support circular economy efforts, especially in the early design phase when opportunities for incorporating these technologies are relatively easy. Traditionally, however, the early design phase has access to very little data about resource flows which makes the introduction of new technologies difficult to do, especially with respect to market-related design decisions. In the later design stages, this data is easier to obtain but is met with increased inflexibility and costs that make these types of changes less common. This paper proposes the use of cyclicity, also known as spectral radius, and NS* minimal-data input metrics that can direct designers to options with the greatest theoretical impact on routing commonly wasted resources back into value circulation. Cyclicity is a metric commonly used in ecology to assess the existence and complexity of cycles, or material/energy pathways that can start and end at the same node, occurring in a system. The metric uses a topological adjacency matrix of resource flows between potential circular economy actors, modeled as a directional graph, and is calculated as the largest absolute eigenvalue of an adjacency matrix and can be a value of zero (no cycles), one (basic cycles), and any value larger than one (increasing presence and complexity of cycles). This study also evaluates actors making up the network as to whether they are part of a strong cycle, a weak component of a cycle, or are disconnected from a cycle, quantified with NS. In a strong cycle, all actors feed into the cycle and the cycle feeds back into the actors. Actors that are weakly connected to a cycle do not contribute to a cyclic pathway. Disconnected actors are not connected to any actor participating in cycling. This paper conducts two case studies on these design tools. The first, a survey of 51 eco-industrial parks (EIPs) and 38 ecological food webs to compare the presence and complexity of cycles in industrial resource systems to ecological resource systems. The latter, food webs, are very effective at retaining value inside the system boundaries. The former, EIPs, were built in support of circular economy principles to use waste streams from one industry as resource streams for others. The analysis shows that 46 out of 51 EIPs had cyclicity values of one or greater and an average of 54% of actors in an EIP are strong. The food webs all have a cyclicity greater than one and an average of 79% of actors in a food web are strong. These results can help decision makers consider CE-supporting pathways earlier in the design process, increasing the likelihood that emerging technologies are incorporated to maximize their CE impact. The second case study explores an emerging technology, Brine Miners, and how cyclicity and NS can be used to guide design decisions to impact the ability of this technology to aid in the creation of a circular economy. The exploration found that focusing on the creation of energy has the potential to add new actors to resource cycling and that diversifying the uses of byproducts creates more complex cycling within a hypothetical economy.

(Paper Number: MSEC2024-125107) “Cyclicity as an Early Circular Economy Design Tool for Emerging Technologies” by Amira Bushagour, Hadear Hassan, and Astrid Layton

Abstract: Reconfigurability in manufacturing signifies a system’s capacity to promptly adapt to evolving needs. This adaptability is critical for markets to maintain operations during unexpected disruptions, including weather anomalies, cyber-attacks, and physical obstructions. Concurrently, the concept of a circular economy is gaining popularity in manufacturing to mitigate waste and optimize resource utilization. Circular economy principles aim to reduce environmental impacts while maximizing economic benefits by emphasizing the reuse of goods and resource byproducts. The nexus between reconfigurability and the circular economy stems from their shared pursuit of sustainability and resilience. Interestingly, biological ecosystems also exhibit these traits, showcasing exceptional adaptability to disturbances alongside the ability to effectively utilize available resources during normal operations. This study explores various manufacturing system configurations to assess both their adaptability and connection to circular economy principles. 44 configurations are categorized based on layout (e.g., job shop, flow line, cellular) and analyzed using convertibility, cyclicity, and Degree of System Order metrics. A significant positive correlation (R2 =0.655) is found between high convertibility and ecologically similar levels of structural cycling, suggesting that effective resource utilization supports adaptability in manufacturing systems. Furthermore, this paper proposes the existence of a possible ” window of vitality” for cyclicity, as it demonstrates a significant correlation (R2 =0.855) between the Degree of System Order and cyclicity. Identifying systems that strike a balance between redundancy, efficiency, convertibility, and cyclicity can aid manufacturing system designers and decision-makers in making choices that address increasing requirements for both sustainability and resilience.

(DOI: 10.1115/1.4065744) “Resilient Circularity in Manufacturing: Synergies between Circular Economy and Reconfigurable Manufacturing” by Hadear Hassan, Amira Bushagour, and Astrid Layton, Journal of Manufacturing Science and Engineering

New Systems Engineering Journal Publication

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A new open access publication is out in the Wiley and INCOSE journal Systems Engineering from BiSSL in collaboration with Dr. Julie Linsey at Georgia Institute of Technology! The article, co-authored by Samuel Blair, Garrett Hairston, Claire Kaat, and Henry Banks and titled “Bio-inspired human network diagnostics: Ecological modularity and nestedness as quantitative indicators of human engineered network function,” investigates the use of modularity and nestedness, 2 analyses that are traditionally used in ecology to study interaction patterns in mutualistic networks (ex. plant-pollinator networks), for human-engineered interaction networks. The paper uses two university engineering makerspaces, modeled as student-tool interaction networks, as case studies to highlight the ability of the approaches to quantitatively monitor the interaction patterns over time and even capture network disturbances (in the case study COVID-19 occurred over the course of data collection).

Abstract:

Analyzing interactions between actors from a systems perspective yields valuable information about the overall system’s form and function. When this is coupled with ecological modeling and analysis techniques, biological inspiration can also be applied to these systems. The diagnostic value of three metrics frequently used to study mutualistic biological ecosystems (nestedness, modularity, and connectance) is shown here using academic engineering makerspaces. Engineering students get hands-on usage experience with tools for personal, class, and competition-based projects in these spaces. COVID-19 provides a unique study of university makerspaces, enabling the analysis of makerspace health through the known disturbance and resultant regulatory changes (implementation and return to normal operations). Nestedness, modularity, and connectance are shown to provide information on space functioning in a way that enables them to serve as heuristic diagnostics tools for system conditions. The makerspaces at two large R1 universities are analyzed across multiple semesters by modeling them as bipartite student-tool interaction networks. The results visualize the predictive ability of these metrics, finding that the makerspaces tended to be structurally nested in any one semester, however when compared to a “normal” semester the restrictions are reflected via a higher modularity. The makerspace network case studies provide insight into the use and value of quantitative ecosystem structure and function indicators for monitoring similar human-engineered interaction networks that are normally only tracked qualitatively.

Blair S, Hairston G, Banks H, Kaat C, Linsey J, Layton A. Bio-inspired human network diagnostics: Ecological modularity and nestedness as quantitative indicators of human engineered network function. Systems Engineering. 2024; 1-13. https://doi.org/10.1002/sys.21756

Annual Conference on Systems Engineering Research (CSER) 2024

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BiSSL Ph.D. student Alexander Duffy will be presenting his research at the annual Conference on Systems Engineering Research (CSER) on March 25-27, 2024 in Tucson, AZ. His paper, titled “Satellite Network Architecture Performance: Setting the Stage for Bio-Inspired Network Design,” covers:

Abstract: Satellite networks, here defined as groups of artificial satellites where the satellites are interconnected by communications links, are increasing in size, number, and criticality. As humanity’s reliance on these networks grows, so too does the need for these networks to be resistant against and quickly recover from disturbances – that is, they need to be resilient. Prior work has found that human networks such as supply chains, water distribution networks, and power grids can improve their resilience by mimicking biological food webs in their design. This paper begins an investigation into whether satellite networks can also benefit from this bio-inspired system approach. The performance of five hypothetical-realistic satellite network case studies is quantified here using global instantaneous coverage, architectural accuracy, and in-network latency. These performance attributes are then compared to the architectural characteristics of biological food webs using Ecological Network Analysis (ENA) metrics, relating species and their predator-prey interactions in a food web to interactions between satellites in a satellite network. The findings suggest that the bio-inspired route holds promise for improving both the performance and resilience of these critical space networks.

Two New BiSSL Papers Published

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Two journal papers related to the use of bio-inspired system design approaches for cyber-physical systems from the BiSSL group have recently been accepted for publication! The 1st stems directly from a current ongoing grant with Sandia National Labs with BiSSL Ph.D. student Emily Payne as co-author and the 2nd is a culmination of multiple collaborations across mechanical and electrical engineering at Texas A&M and is led by former BiSSL Ph.D. student Abheek Chatterjee, now a post-doc at NIST.

Abstract: Cyber-physical systems have behavior that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber-physical systems requires improved understanding of the combined cyber-physical system behavior and methods for holistic analysis. Therefore, we propose leveraging clustering techniques on cyber-physical data from smart grid systems to analyze differences and similarities in behavior during cyber-, physical-, and cyberphysical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber-physical systems. Through this analysis, deeper insights can be shared with decision-makers on what cyber and physical components are strongly or weakly linked, what cyber-physical pathways are most traversed, and the criticality of certain cyber-physical nodes or edges. This paper presents several types of clustering methods for cyber-physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber-physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber-physical graph interdependency analysis.

Jacobs, N., S. Hossain-McKenzie, S. Sun, E. Payne, A. Summers, L. Al Homoud, A. Layton, K. Davis, and C. Goes. (2024) “Leveraging Clustering Techniques for Cyber-Physical System Analysis to Enhance Disturbance Characterization.” The Institution of Engineering and Technology (IET) Cyber-Physical Systems: Theory & Applications.

Abstract: The design of resilient infrastructure is a critical engineering challenge for the smooth functioning of society. These networks are best described as Cyber-Physical Systems of Systems (CPSoS): integration of independent constituent systems, connected by physical and cyber interactions, to achieve novel capabilities. Bio-inspired design, using a framework called the Ecological Network Analysis (ENA), has been shown to be a promising solution for improving the resilience of engineering networks. However, the existing ENA framework can only account for one type of flow in a network. Thus, it is not yet applicable for the evaluation of CPSoS. The present work addresses this limitation by proposing a novel multigraph model of CPSoS, along with guidelines and modified metrics that enable ENA evaluation of the overall (cyber and physical) network organization of the CPSoS. The application of the extended framework is demonstrated using an energy infrastructure case study. This research lays the critical groundwork for investigating the design of resilient CPSoS using biological ecosystems inspiration.

Chatterjee, A., H. Huang, R. Malak, K. Davis, and A. Layton. (2024) “Extending Ecological Network Analysis to Design Resilient Cyber-Physical System of Systems.” IEEE Open Journal of Systems Engineering.