Enabling Resilience in Cyber-Physical-Human Water Infrastructures

Rapid urbanization and growth in urban populations have forced community-scale infrastructures (e.g., water, power and natural gas distribution systems, and transportation networks) to operate at their limits. Aging (and failing) infrastructures around the world are becoming increasingly vulnerable to operational degradation, extreme weather, natural disasters and cyber attacks/failures. These trends have wide-ranging socioeconomic consequences and raise public safety concerns. In this thesis, we introduce the notion of cyber-physical-human infrastructures (CPHIs) - smart community-scale infrastructures that bridge technologies with physical infrastructures and people. CPHIs are highly dynamic stochastic systems characterized by complex physical models that exhibit regionwide variability and uncertainty under disruptions. Failures in these distributed settings tend to be difficult to predict and estimate, and expensive to repair. Real-time fault identification is crucial to ensure continuity of lifeline services to customers at adequate levels of quality. Emerging smart community technologies have the potential to transform our failing infrastructures into robust and resilient future CPHIs.In this thesis, we explore one such CPHI - community water infrastructures. Current urban water infrastructures, that are decades (sometimes over a 100 years) old, encompass diverse geophysical regimes. Water stress concerns include the scarcity of supply and an increase in demand due to urbanization. Deterioration and damage to the infrastructure can disrupt water service; contamination events can result in economic and public health consequences. Unfortunately, little investment has gone into modernizing this key lifeline.To enhance the resilience of water systems, we propose an integrated middleware framework for quick and accurate identification of failures in complex water networks that exhibit uncertain behavior. Our proposed approach integrates IoT-based sensing, domain-specific models and simulations with machine learning methods to identify failures (pipe breaks, contamination events). The composition of techniques results in cost-accuracy-latency tradeoffs in fault identification, inherent in CPHIs due to the constraints imposed by cyber components, physical mechanics and human operators. Three key resilience problems are addressed in this thesis; isolation of multiple faults under a small number of failures, state estimation of the water systems under extreme events such as earthquakes, and contaminant source identification in water networks using human-in-the-loop based sensing. By working with real world water agencies (WSSC, DC and LADWP, LA), we first develop an understanding of operations of water CPHI systems. We design and implement a sensor-simulation-data integration framework AquaSCALE, and apply it to localize multiple concurrent pipe failures. We use a mixture of infrastructure measurements (i.e., historical and live water pressure/flow), environmental data (i.e., weather) and human inputs (i.e., twitter feeds), combined and enhanced with the domain model and supervised learning techniques to locate multiple failures at fine levels of granularity (individual pipeline level) with detection time reduced by orders of magnitude (from hours/days to minutes). We next consider the resilience of water infrastructures under extreme events (i.e., earthquakes) - the challenge here is the lack of apriori knowledge and the increased number and severity of damages to infrastructures. We present a graphical model based approach for efficient online state estimation, where the offline graph factorization partitions a given network into disjoint subgraphs, and the belief propagation based inference is executed on-the-fly in a distributed manner on those subgraphs. Our proposed approach can isolate 80% broken pipes and 99% loss-of-service to end-users during an earthquake.Finally, we address issues of water quality - today this is a human-in-the-loop process where operators need to gather water samples for lab tests. We incorporate the necessary abstractions with event processing methods into a workflow, which iteratively selects and refines the set of potential failure points via human-driven grab sampling. Our approach utilizes Hidden Markov Model based representations for event inference, along with reinforcement learning methods for further refining event locations and reducing the cost of human efforts.The proposed techniques are integrated into a middleware architecture, which enables components to communicate/collaborate with one another. We validate our approaches through a prototype implementation with multiple real-world water networks, supply-demand patterns from water utilities and policies set by the U.S. EPA. While our focus here is on water infrastructures in a community, the developed end-to-end solution is applicable to other infrastructures and community services which operate in disruptive and resource-constrained environments

Collective attention in online social networks

Social media is an ever-present tool in modern society, and its widespread usage positions it as a valuable source of insights into society at large. The study of collective attention in particular is one application that benefits from the scale of social media data. In this thesis we will investigate how collective attention manifests on social media and how it can be understood. We approach this challenge from several perspectives across network and data science. We first focus on a period of increased media attention to climate change to see how robust the previously observed polarised structures are under a collective attention event. Our experiments will show that while the level of engagement with the climate change debate increases, there is little disruption to the existing polarised structure in the communication network. Understanding the climate media debate requires addressing a methodological concern about the most effective method for weighting bipartite network projections with respect to the accuracy of community detection. We test seven weighting schemes on constructed networks with known community structure and then use the preferred methodology we identify to study collective attention in the climate change debate on Twitter. Following on from this, we will investigate how collective attention changes over the course of a single event over a longer period, namely the COVID-19 pandemic. We measure how the disruption to in-person social interactions as a consequence of attempts to limit the spread of COVID-19 in England and Wales have affected social interaction patterns as they appear on Twitter. Using a dataset of tweets with location tags, we will see how the spatial attention to locations and collective attention to discussion topics are affected by social distancing and population movement restrictions in different stages of the pandemic. Finally we present a new analysis framework for collective attention events that allows direct comparisons across different time and volume scales, such as those seen in the climate change and COVID-19 experiments. We demonstrate that this approach performs better than traditional approaches that rely on binning the timeseries at certain resolutions and comment on the mechanistic properties highlighted by our new methodology.Engineering and Physical Sciences Research Council (EPSRC

Modelling and Design of Resilient Networks under Challenges

Communication networks, in particular the Internet, face a variety of challenges that can disrupt our daily lives resulting in the loss of human lives and significant financial costs in the worst cases. We define challenges as external events that trigger faults that eventually result in service failures. Understanding these challenges accordingly is essential for improvement of the current networks and for designing Future Internet architectures. This dissertation presents a taxonomy of challenges that can help evaluate design choices for the current and Future Internet. Graph models to analyse critical infrastructures are examined and a multilevel graph model is developed to study interdependencies between different networks. Furthermore, graph-theoretic heuristic optimisation algorithms are developed. These heuristic algorithms add links to increase the resilience of networks in the least costly manner and they are computationally less expensive than an exhaustive search algorithm. The performance of networks under random failures, targeted attacks, and correlated area-based challenges are evaluated by the challenge simulation module that we developed. The GpENI Future Internet testbed is used to conduct experiments to evaluate the performance of the heuristic algorithms developed

ENHANCING RESILIENCE OF COMPLEX NETWORKS: WASHINGTON D.C. URBAN RAIL TRANSIT AS A CASE STUDY

According to the United Nation’s Department of Economic and Social Affairs Population Division, 66% of the world’s population will reside in urban areas by 2050; a boost from 30 % in 1950. Urbanization has indeed triumphed and its speed has brought innovation and economic growth. Its synergies within infrastructure systems are undeniable and have increased the demand for such systems. However, urbanization is one reason infrastructure systems are knocked out of equilibrium and show complex dynamical behavior. Most infrastructure systems have been designed without planning for this magnitude of potential demographic changes; thus redesigns are long overdue. Also, climate change looms. Resource scarcity and host of other factors leave their impacts; all pose some incidence of perturbation in the state of the infrastructure system. These perturbations can affect the system’s resilience, which is a defining property of each system for remaining functional in the midst of disruption from an adverse event. Therefore, it is essential to develop appropriate metrics and methods to enhance the resilience of infrastructures at the network level. Such enhancements are critical for sustainable infrastructure development that is capable of performing satisfactorily through intentional and/or stochastic disruptions. A resilience evaluation of a network typically entails assessing vulnerability and robustness as well as identifying strategies to increasing network efficiency and performance and offering recovery strategies ideally taken in a cost-effective manner. This dissertation uses complex network theory (CNT) as the theoretic basis to enhance the resilience of large-scale infrastructure networks, such as urban rail transit systems. Urban rail transit infrastructures are heterogeneous, complex systems consisting of a large number of interacting nodes and links, which can imitate a network paradigm. Any adverse event leading to a disruption in the interaction and connectivity of network components would dramatically affect the safety and wellbeing of commuters, as well as the direct and indirect costs associated with performance loss. Therefore, enhancing their resilience is necessary. Using the Washington D.C. Urban rail transit as a case study, this dissertation develops a methodology to analyze network topology, compute its efficiency, vulnerability, and robustness in addition to provide a unified metric for assessing the network resilience. The steps of methodology are applied to two models of weighted and unweighted networks. For the weighted model two novel algorithms are proposed to capture the general pattern of ridership in the network, and to reflect the weights on assessing network efficiency, respectively. This dissertation then proposes an effective strategy to increase the network resilience prior to a disruptive event, e.g., a natural disaster, by adding several loop lines in the network for topological enhancement. As such, adding a loop line can create redundancy to the vulnerable components and improve network resilience. Expanding on this, the dissertation offers comparative recovery strategies and cost model in the case of disruption. An effective recovery strategy must demonstrate rapid optimal restoration of a disrupted system performance while minimizing recovery costs. In summary, the systematic methodology described above, assesses and enhances the network resilience. The initial results rank the most vulnerable and robust components of the network. The algorithms developed throughout the study advance the weighted network analysis state of art. The topological enhancement strategy offered basis to justify capital improvement. Post failure recovery analysis and the cost model serves to inform decision makers in identifying best recover strategies with special attention not only to restoring performance of a system but also on reducing associated failure and recovery costs. The use of the methodology proposed in this dissertation may lead to significant societal benefits by reducing the risk of catastrophic failures, providing references for mitigation of disruption due to adverse events, and offering resilience- based strategies, and related pursuits

A Systems-Level Approach to Understand The Seasonal Factors Of Early Development With Clinical and Pharmacological Applications

Major developmental defects occur in 100,000 to 200,000 children born each year in the United States of America. 97% of these defects are from unidentified causes. Many fetal outcomes (e.g., developmental defects), result from interactions between genetic and environmental factors. The lifetime effects from prenatal exposures with low impact (e.g., air pollution) are often understudied. Even when these exposures are studied, the focus is often placed on immediate effects of the exposure (e.g., fetal anomalies, miscarriage rates) leaving lifetime effects largely unexplored. This makes prolonged (or lifetime) effects of low-impact exposures an understudied research area. Included in this set of low-impact exposures is seasonal variance at birth. This thesis measures the effects of seasonal variance at birth on lifetime disease risk at both the population-level and molecular-levels. Four aims, comprising this thesis study, were conducted that utilize data from pharmacology, clinical care (Electronic Health Records) and genetics. These aims included: 1.) Development of an Algorithm to Reveal Diseases with a Prenatal/Perinatal Seasonality Component (described in chapter 2); 2.) Investigation of Climate Variables that Affect Lifetime Disease Risk By Altering Environmental Drivers (described in chapters 3 and 4); 3.) Discovery of Genes Involved in Birth Season – Disease Effects (described in chapter 5) and 4.) Investigation of Pharmacological Inhibitors As Phenocopies of the Birth Season – Disease Effect (described in chapters 6 and 7). Knowledge gained from these four areas, through seven distinct studies, establishes that birth season is a causal risk factor in a number of common diseases including cardiovascular diseases

The Medieval Globe 1 (2014) - Pandemic Disease in the Medieval World: Rethinking the Black Death

The plague organism (Yersinia pestis) killed an estimated 40% to 60% of all people when it spread rapidly through the Middle East, North Africa, and Europe in the fourteenth century: an event known as the Black Death. Previous research has shown, especially for Western Europe, how population losses then led to structural economic, political, and social changes. But why and how did the pandemic happen in the first place? When and where did it begin? How was it sustained? What was its full geographic extent? And when did it really end? Pandemic Disease in the Medieval World is the first to synthesize the new evidence and research methods that are providing fresh answers to these crucial questions. It was only in 2011, thanks to ancient DNA recovered from remains unearthed in London’s East Smithfield cemetery, that the full genome of the plague pathogen was identified. This single-celled organism probably originated 3000-4000 years ago and has caused three pandemics in recorded history: the Justinianic (or First) Plague Pandemic, around 541-750; the Black Death (Second Plague Pandemic), conventionally dated to the 1340s; and the Third Plague Pandemic, usually dated from around 1894 to the 1930s. This ground-breaking book brings together scholars from the humanities and social and physical sci­ences to address the question of how recent work in genetics, zoology, and epi­de­miology can enable a rethinking of the Black Death\u27s global reach and its larger historical significance. It forms the inaugural double issue of The Medieval Globe, a new journal sponsored by the Program in Medieval Studies at the University of Illinois at Urbana-Champaign. This issue of The Medieval Globe is published with the support of the World History Center at the University of Pittsburgh

TMG 1 (2014): Pandemic Disease in the Medieval World: Rethinking the Black Death, ed. Monica Green

The plague organism (Yersinia pestis) killed an estimated 40% to 60% of all people when it spread rapidly through the Middle East, North Africa, and Europe in the fourteenth century: an event known as the Black Death. Previous research has shown, especially for Western Europe, how population losses then led to structural economic, political, and social changes. But why and how did the pandemic happen in the first place? When and where did it begin? How was it sustained? What was its full geographic extent? And when did it really end? Pandemic Disease in the Medieval World is the first book to synthesize the new evidence and research methods that are providing fresh answers to these crucial questions. It was only in 2011, thanks to ancient DNA recovered from remains unearthed in London’s East Smithfield cemetery, that the full genome of the plague pathogen was identified. This single-celled organism probably originated 3000-4000 years ago and has caused three pandemics in recorded history: the Justinianic (or First) Plague Pandemic, around 541-750; the Black Death (Second Plague Pandemic), conventionally dated to the 1340s; and the Third Plague Pandemic, usually dated from around 1894 to the 1930s. This ground-breaking book brings together scholars from the humanities and social and physical sci­ences to address the question of how recent work in genetics, zoology, and epi­de­miology can enable a rethinking of the Black Death\u27s global reach and its larger historical significance. It forms the inaugural double issue of The Medieval Globe, a new journal sponsored by the Program in Medieval Studies at the University of Illinois at Urbana-Champaign. This issue of The Medieval Globe is published with the support of the World History Center at the University of Pittsburgh.https://scholarworks.wmich.edu/medieval_globe/1000/thumbnail.jp

Metropolitan Research

Metropolitan research requires multidisciplinary perspectives in order to do justice to the complexities of metropolitan regions. This volume provides a scholarly and accessible overview of key methods and approaches in metropolitan research from a uniquely broad range of disciplines including architectural history, art history, heritage conservation, literary and cultural studies, spatial planning and planning theory, geoinformatics, urban sociology, economic geography, operations research, technology studies, transport planning, aquatic ecosystems research and urban epidemiology. It is this scope of disciplinary - and increasingly also interdisciplinary - approaches that allows metropolitan research to address recent societal challenges of urban life, such as mobility, health, diversity or sustainability