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Controlling interdependent meso-nanosecond dynamics and defect generation in metal 3D printing
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Efficient Interdependent Systems Recovery Modeling with DeepONets
Modeling the recovery of interdependent critical infrastructure is a key component of quantifying and optimizing societal resilience to disruptive events. However, simulating the recovery of large-scale interdependent systems under random disruptive events is computationally expensive. Therefore, we propose the application of Deep Operator Networks (DeepONets) in this paper to accelerate the recovery modeling of interdependent systems. DeepONets are ML architectures which identify mathematical operators from data. The form of governing equations DeepONets identify and the governing equation of interdependent systems recovery model are similar. Therefore, we hypothesize that DeepONets can efficiently model the interdependent systems recovery with little training data. We applied DeepONets to a simple case of four interdependent systems with sixteen states. DeepONets, overall, performed satisfactorily in predicting the recovery of these interdependent systems for out of training sample data when compared to reference results.
Resilience of Interdependent Water and Power Systems: A Literature Review and Conceptual Modeling Framework
As increasing pressures of population growth and climate change arise, water and power systems (WPS) are becoming increasingly interdependent. This interdependency has resulted in an increased potential for cascading failures, whereby the service interruption of one system can propagate to interdependent ones. This paper makes four contributions. First, we present an extensive literature review in the field of integrated water and power resilience, leveraging both institutional and technical literature research landscapes. We compare various modeling approaches used to model interdependent WPS and discuss the different metrics and definitions that are typically employed to quantify and define resilience. Relevant challenges and gaps related to modeling tools and metrics are also discussed, and appropriate recommendations are made. Second, the paper presents a visualization prototype for interdependent WPS to showcase water and power system interdependencies and reveal co-managed resilience strategies that can be used to improve resilience under different types of common threats. Third, we provide a conceptual decision support framework that simultaneously optimizes a portfolio of co-managed resilience strategies in the face of multiple, uncertain threats and addresses WPS interdependencies. Finally, we present future trends regarding digitalization, integrated planning, collaborative governance, and equity needs for building more resilient WPS.
A Mathematical Framework for Representing Cyber-Physical System Interdependencies and Resilience
U.S. critical infrastructure is increasingly composed of integrated cyber-physical systems (CPS) whose components are operationally interdependent. These interdependencies can create additional vulnerabilities beyond those that are typically evaluated through independent physical and cyber risk assessments. These additional vulnerabilities, previously undetected at the sub-system and/or component level, could cause cascading effects across both the physical and cyber domains that could lead to infrastructure disruption or failure. This paper will review an initial mathematical framework developed to help understand the dynamics of these interfacing physical and cyber components at a holistic system level. The resulting framework also provides a method for modeling and assessing the resilience of CPS in complex and adaptive systems. It frames the system in terms of mission performance and the potential effects to performance caused by attacks that are cyber, physical, or blended in nature. The framework utilizes a systems-based, state-space modeling approach to reason about risk, resilience, and interdependencies in CPS. Probabilities are applied to each CPS component regarding a threat (both man-made and natural) and the likelihood of that particular component being impacted, along with indirect impact to other interdependent components. These component-level impacts from threats then translate into mission-level impact and systemic state change.
Energy-water interdependencies across the three major United States electric grids: A multi-sectoral analysis
As water availability and timing of delivery fluctuates and the US electric grid sees rapid transformation and reconfiguration under decarbonization and resource adequacy strategies, there is a critical need for information and data that supports understanding the water-energy interdependency landscape. The United States currently lacks comprehensive data, informative visualizations, and analysis of energy-water interdependencies at scales necessary to support resource and operational decision-making. This article provides US electricity interconnection-level Sankey diagrams that show the relative reliance of water and energy across various economic sectors. A deeper analysis is additionally provided at the county level to illustrate trends and potential opportunities related to resiliency and efficiency in multi-sectoral water and energy flow distributions and intensities. We find that the electricity interconnections in the US vary dramatically in their water and energy interdependencies across applications and economic sectors.
Component Importance and Interdependence Analysis for Transmission, Distribution and Communication Systems
For critical infrastructure restoration planning, the real-time scheduling and coordination of system restoration efforts, the key in decision-making is to prioritize those critical components that are out of service during the restoration. For this purpose, there is a need for component importance analysis. While it has been investigated extensively for individual systems, component importance considering interdependence among transmission, distribution and communication (T&D&C) systems has not been systematically analyzed and widely adopted. In this study, we propose a component importance assessment method in the context of interdependence between T&D&C networks. Analytic methods for multilayer networks and a set of metrics have been applied for assessing the component importance and interdependence between T&D&C networks based on their physical characteristics. The proposed methodology is further validated with integrated synthetic Illinois regional transmission, distribution, and communication (T&D&C) systems, the results reveal the unique characteristics of component/node importance, which are strongly affected by the network topologies and cross-domain node mapping.
interflow: A Python package to organize, calculate, and visualize sectoral interdependency flow data
Many economic sectors rely on an uninterrupted “upstream” supply of a resource to conduct their primary functions, leaving them vulnerable to adverse effects should that resource flow be interrupted or compromised (OECD, 2017; U.S. EPA, 2010). Well-known examples of these relationships include water demand by the energy sector (e.g., thermoelectric cooling for nuclear generation) (Grubert & Sanders, 2018; Webber, 2017) and energy demand by the water sector (e.g., electricity required to treat or move water in the public water sector) (Congressional Research Service, 2017) though many others exist. Being able to calculate and document these interdependencies and evaluate where the greatest cross-sectoral intensities and flows exist can reveal opportunities to enhance the overall network. Despite the implications and potential impacts, however, these interconnections and flows have been historically complex to analyze and understand. The interflow package provides a flexible tool to organize, calculate, and visualize (using Sankey diagrams and other visualizations) sectoral interdependency flows for multiple subsectors and resources. This tool can help decision-makers, researchers, and other audiences more easily pull meaning from these interdependencies to reveal multi-faceted opportunities and risks. interflow can help investigate questions such as (1) which sectors have high cross-resource dependencies, (2) how does demand for a resource in various sectors compare across regions, and (3) where the sectoral and regional opportunities are for enhanced efficiency, security, and resiliency.
Efficient Sampling of Complex Interdependent and Multiplex Networks
Efficient sampling of interdependent and multiplex infrastructure networks is critical for effectively applying failure and recovery algorithms in real-world settings, as well as to generate property-preserving reduced-order graph-based ensembles that address topological uncertainties. In this paper, we first explore the performance, i.e. the success in preserving graph properties, of graph sampling algorithms for interdependent and multiplex networks with synthetic and real-world graphs. We simulate sampling algorithms under different parameter settings. These settings include probabilistic graph generators, coupling patterns, and various performance metrics. Our results show that while Random Node and Random Walk sampling algorithms perform best for interdependent networks, Random Edge and Forest Fire sampling algorithms perform best for multiplex networks. Second, we propose and implement a novel similarity-based sampling algorithm for multiplex networks that samples only log(N) number of layers of an N-layer multiplex network while yielding computational savings with performance guarantees. Experimental results show that similarity sampling outperforms complete sampling of all layers while decreasing performance costs from a linear scale to a logarithmic one. Our results also indicate that similarity-based sampling outperforms complete sampling and random selection in nearly all scenarios when tested with real-world data.
Seeking help for perinatal depression and anxiety: a systematic review of systematic reviews from an interdependent perspective
Abstract Background Seeking help for perinatal mood and anxiety disorders is crucial for women’s mental health and babies’ development, yet many women do not seek help for their condition and remain undiagnosed and untreated. This systematic review of systematic reviews aimed at summarizing and synthesizing findings from all systematic reviews on seeking help for PMAD in the context of interdependence theory, highlighting the interdependent relationship between women and healthcare providers and how it may impact women’s seeking-help process. Methods Four electronic databases were searched, and 18 studies published up to 2023 met inclusion criteria for review. Results The capability, opportunity and motivation model of behavior was used as a framework for organizing and presenting the results. Results demonstrate that seeking help for PMAD is a function of the interdependent relationship between perinatal women’s and healthcare providers’ psychological and physical capabilities, social and physical opportunities, and their reflective and automatic motivation. Conclusions Unmet needs in perinatal mental healthcare is an important public health problem. This systematic review of systematic reviews highlights key factors for policymakers, researchers, and practitioners to consider to optimize healthcare systems and interventions in a way that enhances perinatal women’s treatment whenever necessary.
Resilience Analytics for Interdependent Power and Water Distribution Systems
The occurrence of large-scale outages in power distribution systems (PDSs) caused, for instance, by extreme weather events has raised concerns on the cascading impact of the power outages on the resilience of other critical infrastructures, including water distribution systems (WDSs), whose healthy operation highly relies on the availability of power from PDSs. Here, this paper proposes an analytics model for quantifying the interdependence between the resilience of power and water distribution systems. The proposed model first performs spatio-temporal outage analysis on the PDS and WDS to determine the PDS power serving capability and the resulting cascading impact on the WDS water serving capability during the occurrence of disruptions. The resilience interdependence is then determined by a proposed set of six metrics calculated using the power and water serving capability profiles, which trace and quantify the cascading impacts of power outages in the operation of WDSs during the degradation and recovery stages. The proposed analytics model is implemented on the IEEE 33-bus power distribution system supplying power for pumping stations of a 15-node test WDS, considering multiple cases of water storage capacity, single and multiple outage scenarios, and availability of distributed generation. The numerical results demonstrate that the proposed metrics track the temporal and spatial interdependence between the resilience of power and water distribution systems, providing a mechanism to identify weak spots, and assess options to enhance the resilience of the critical water infrastructure.
InterGraph-CPS: A Graph-Theoretic Approach to Characterize Cross-Domain Cyber-Physical Interdependencies and Uncertainties in Electric Grid Systems for Improved Decision-Making in Operation and Response
Critical infrastructure systems such as the electric grid are increasingly cyber-physical; yet, despite the cyber-physical characteristics of critical infrastructure systems, the physical process system and communication/control network system are traditionally analyzed in siloes. As these systems become more cyber-physical, it is crucial that models and methods are available to assess the cyber physical system (CPS) interdependencies, characteristics, and event propagation for improved planning, operation, and response. Thus, we proposed an integrated structural and temporal CPS interdependency analysis framework, InterGraph-CPS, that provides insight into the CPS function during normal operation as well as disturbances. This integrated structural and temporal interdependency framework is uniquely designed for assessing CPSs by account for the challenges of analyzing cyber and physical data streams together due to data availability, data type, and time scale differences. By leveraging both structural (e.g., graph analysis) and temporal (e.g., data analytics) techniques, different CPS behaviors and configurations can be accounted for.
Resilience-Motivated Distribution System Restoration Considering Electricity-Water-Gas Interdependency
A major outage in the electricity distribution system may affect the operation of water and natural gas supply systems, leading to an interruption of multiple services to critical customers. Therefore, enhancing resilience of critical infrastructures requires joint efforts of multiple sectors. In this paper, a distribution system service restoration method considering the electricity-water-gas interdependency is proposed. The objective is maximizing the supply of electricity, water, and gas to critical customers after an extreme event. The operational constraints of electricity, water, and natural gas networks are considered. Additionally, the characteristics of electricity-driven coupling components, including water pumps and gas compressors, are also modeled. Relaxation techniques are applied to non convex constraints posed by physical laws of those networks. Consequently, the restoration problem is formulated as a mixed-integer second-order cone program, which can readily be solved by the off-the-shelf solvers. The proposed method is validated by numerical simulations on an electricity-water-gas integrated system, developed based on benchmark models of the subsystems. The results indicate that considering the interdependency refines the allocation of limited generation resources and demonstrate the exactness of the proposed convex relaxation
Towards Optimal and Executable Distribution Grid Restoration Planning With a Fine-Grained Power-Communication Interdependency Model
Distribution service restoration (DSR) under natural disasters is always a critical and challenging problem for utility companies. An effective solution must not ignore the power-communication interdependency as various systems are getting increasingly connected in the Smart Grid era. In this paper, we propose a two-layer distribution system model with both power and communication components. Based on this model, we formulate the restoration process as a routing problem that schedules the path and action sequence of utility crews that involves repairing damaged components, closing power switches, and enabling communication paths between the control center and remote field devices. Further, we develop a simulation-based method to quantitatively evaluate the restoration process with public reference models of large-scale power systems. The experimental results show that our method improves the total restored energy up to 57.6% and reduces the recovery time up to 63% by considering the power-communication interdependency.
A tri-level optimization model for interdependent infrastructure network resilience against compound hazard events
Resilient operation of interdependent infrastructures against compound hazard events is essential for maintaining societal well-being. To address consequence assessment challenges in this problem space, we propose a novel policy-guided tri-level optimization model applied to a proof-of-concept case study with fuel distribution and transportation networks – encompassing one realistic network; one fictitious, yet realistic network; as well as networks drawn from three synthetic distributions. Mathematically, our approach takes the form of a defender-attacker-defender (DAD) model—a multi-agent tri-level optimization, comprised of a defender, attacker, and an operator acting in sequence. Here, in this study, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations. A notional attacker aims to hypothetically disrupt normal operations by reducing supply at the supply terminals, and the notional defender aims to identify best proxy defense policy options which include hardening supply terminals or allowing alternative distribution methods such as trucking reserve supplies. We solve our DAD formulation at a metropolitan scale and present practical defense policy insights against hypothetical compound hazards. We demonstrate the generalizability of our framework by presenting results for a realistic network; a fictitious, yet realistic network; as well as for three networks drawn from synthetic distributions. Additionally, we demonstrate the scalability of the framework by investigating runtime performance as a function of the network size. Steps for future research are also discussed.
Water & Energy Interdependencies Across Economic Sectors of the United States
The motivation of the Integrated Water Power Resilience Project is to identify and develop opportunities for improving resilience in the water and power sectors through coordinated planning, investment, and operations and thereby provide benefits to power and water utilities, consumers, and the environment. Water and power systems are interdependent, subject to many of the same natural and manmade hazards, and are critical for the well-being of communities and society. Because of the interconnectedness of water and power systems, there are substantial economic, social, and environmental benefits to co-managing the market sectors for resilience instead of managing them separately. To support this initiative and future research in this area, a county level water and energy flow dataset was developed to build, calculate, and visualize interconnections in water and energy between various sectors. This report presents county and regional water and energy visualizations across nine economic sectors of the United States: agriculture, commercial, electricity generation, industrial, mining, public water supply, residential, transportation, and wastewater treatment.
Atomistic understanding of interdependence of crystal growth of two seeds embedded in glass
Recent results showed that during growth, a crystal seed embedded in glass experiences nonuniform forces from the host matrix. This result raised the possibility that during devitrification of glass, the growth of a crystal can be affected by the presence of neighboring crystals. To investigate the nature of any such interdependence of growth of neighboring crystals, atomic scale morphology of two seeds growing in a glass matrix was simulated as a function of their separation and relative misorientation. The results show a coupled rotation of seeds driven by (1) non-uniform forces from the surrounding glass, and (2) mutual interactions between the seeds. At small separations and low misorientation, each seed rotates toward perfect alignment, ultimately forming a single crystal. The onset and rate of alignment, as well as subsequent crystal growth, are strongly dependent on the growth temperature. In contrast, at larger separations and higher misorientation, the seeds cannot achieve full alignment before coalescing, resulting in polycrystalline structures.
Interdependence in active mobility adoption: Joint modeling and motivational spillover in walking, cycling and bike-sharing
Active mobility offers an array of physical, emotional, and social well-being benefits. However, with the proliferation of the sharing economy, new nonmotorized means of transport are entering the fold, complementing some existing mobility options while competing with others. The purpose of this research study is to investigate the adoption of three active travel modes—namely walking, cycling, and bikesharing—in a joint modeling framework. Here, the analysis is based on an adaptation of the stages of change framework, which originates from the health behavior sciences. Multivariate ordered probit modeling drawing on U.S. survey data provides well-needed insights into individuals’ preparedness to adopt multiple active modes as a function of personal, neighborhood, and psychosocial factors. The research suggests three important findings. (1) The joint model structure confirms interdependence among different active mobility choices. The strongest complementarity is found for walking and cycling adoption. (2) Each mode has a distinctive adoption path with either three or four separate stages. We discuss the implications of derived stage-thresholds and plot adoption contours for selected scenarios. (3) Psychological and neighborhood variables generate more coupling among active modes than individual and household factors. Specifically, identifying strongly with active mobility aspirations, experiences with multimodal travel, possessing better navigational skills, along with supportive local community norms are the factors that appear to drive the joint adoption decisions. This study contributes to the understanding of how decisions within the same functional domain are related and help to design policies that promote active mobility by identifying positive spillovers and joint determinants.