In this submission, the authors develop an innovative approach to measuring community resilience by mathematical analysis of its members’ social-media microblogs. The approach involves applying machine-learning and graph-analytic techniques to infer social cohesion, which is later used as the state variable by which resilience is measured. We analyze community cohesion and its dynamics during two natural disasters that hit San Francisco Bay Area with an interval of only two years - the wildfires of 2020 and the torrential rainstorms during the water year of 2022/23.The backdrop of the wildfires was characterized by the first year of the COVID pandemic, with all the uncertainty, deficit of personal protective equipment (PPE), loss of jobs, social-justice protests, and Presidential elections. For the rainstorms, the backdrop consisted of the Omicron variant of COVID, structural damage due to heavy rains and winds, and midterm elections. Bay Area economy too was in a different state during the wildfires than it was during the rainstorms. In this submission, we measure the community resilience based on the dynamics of Bay Area recovering from these events. We propose novel metrics for community cohesion and investigate the mechanisms by which emotions, local economy, weather, and air quality affects community cohesion. We also explore whether community resilience is influenced by these mechanisms.Specifically, we analyze the mediating role played by emotions in the community cohesion and resilience processes.

Transportation infrastructure networks are prone to disruptions, most of which are beyond control. However, the spread of disinformation can worsen downtime in these systems by indirectly causing disruptions, such as station closures or rerouting of services based on false reports. The relationship between disinformation and the service disruptions is very important with reference to enhancing the resilience of transportation systems. This paper contributes to the field by applying artificial intelligence techniques to analyze how disinformation impacts service disruptions, particularly focusing on the Port Authority Trans-Hudson (PATH) system in New Jersey and New York, providing insights for improving operational responsiveness. The disruption operational impacts of disinformation are analyzed using several data sources, including schedules, ridership reports, and real-time alerts. A machine learning-based K-means algorithm framework is applied to cluster disruption alerts from social media. Disruption scenarios dominated by disinformation are identified using advanced natural language processing (NLP) methods, specifically BERTopic and Latent Dirichlet Allocation (LDA) topic modeling techniques. A Monte Carlo simulation is applied to quantify the effects of this dominant disinformation-induced disruption scenario on the commuter time and costs. This study reveals that disinformation significantly influences infrastructure reliability and points out the necessity for effective strategies to combat its impacts. The findings reveal the importance of transportation disruptions to the functioning of the transportation system and emphasize the need for robust measures to reduce the adverse effects, hence making the system to be more resilient and secure in the public’s perception.

Social media provides valuable insights into societal opinions and user interactions. Social landscapes, created from these interactions, offer a comprehensive view of online conversations and social media dynamics. The development of advanced data analytics tools has made the creation of social landscapes for larger populations increasingly common and accessible for researchers. This underscores the importance of clearly defining and organizing the insights we seek from social landscapes. We introduce a methodology to analyze the social structure through social landscapes. We have identified 12 key elements that encapsulate the insights expected from social landscapes. Then, we integrate two comprehensive social landscape approaches into our methodology that effectively provide insights into the key elements previously outlined. These two approaches illuminate different facets of social media dynamics: one focuses on the content generated and the other on relationship-based interactions. First, we revisit the concept of galaxies as a single time-based snapshot of large-scale online conversations. Second, we introduce a technique that utilizes the network of user interactions on social media to map social structures. We propose a novel method to construct a sociopolitical spectrum using discourse trajectory inference (pseudotime transformation), marking its first use outside bioinformatics literature. Finally, we take the introduced methodology to evaluate how each social landscape enhances our understanding of social media dynamics. These insights can help to structure the insights we aim to extract from social landscapes and provide practical tools for media analysts and strategists aiming to analyze social media dynamics effectively.

Organizations increasingly recognize the pivotal role of knowledge and relationships in driving effective communication, collaboration, and innovation. However, existing approaches for knowledge-based social network analysis often rely on intrusive or labor-intensive data collection methods, which restricts their practical application. This study presents a novel framework for the generation and scrutiny of knowledge networks within organizations. Unlike the existing methods, our proposed approach leverages readily available administrative data, obviating the need for intrusive employee monitoring. This feature enables continuous organizational monitoring of intellectual capital and aids in predicting the ramifications of future staffing changes. Furthermore, our novel adaptable network weighting method provides a nuanced view of the knowledge and relational dynamics that are often not detected by traditional approaches. By utilizing flow-based centrality metrics, the model captures the emergent structural properties that may otherwise be overlooked. Thus the proposed framework offers a holistic, flexible, and efficient tool for mapping and understanding organizational knowledge dynamics.

In times of shrinking margins, researchers are looking for more cost-effective and profitable ways to improve the post-production support of large-scale, complex systems. There is an inherent tradeoff between the system design and the design’s long-term support. A system’s design largely determines its reliability that, in turn, influences the demands on its post-production support network necessary to maintain the proper use of the system over its intended, economic useful-life. Performance-based contracts are a successful financial instrument between suppliers and buyers for long-term support contracts. This research leverages the tenets of performance-based contracting, especially its foundation in transactional cost economics and management control theory, and agency theory, to develop and test an analytical model. This research proposes a novel, analytical model that maximizes the profit margin of a large-scale, complex system simultaneously considering its design and post-production support network. To date, these decisions are largely understudied. This model uses redundancy allocation to represent a design decision, and the post-production support network decisions are the location, quantity of spares, and logistics footprint. The post-production support network is a non-arboreal, multi-echelon sustainment network, and the design is a series-parallel configuration. The model is constrained by customer-specified, minimum reliability, mean-time-between-failure (MTBF), and a maximum logistics footprint (LF) measured in pounds. A meta-heuristic algorithm was used to address the nonlinearity of the objective function and constraints. Afterward, a numerical example was solved, and comparative experiments were conducted to test the algorithm. The results showed a profit for the supplier of $14,505.12 with 78.45 hrs of MTBF out of the 75 hours minimum allowed and a logistic footprint of 6,285.25 lb. out of the 10,000 lb allowed. The solution demonstrates the economic importance of system engineers, contract personnel, and program managers in understanding the inherent tradeoff space connecting the design and support of a system.

Using automated data analysis to understand what makes a play successful in football can enable teams to make data-driven decisions that may enhance their performance throughout the season. Analyzing different types of plays (e.g., corner, penalty, free kicks) requires different considerations. This work focuses on the analysis of corner kick plays. However, the central ideas apply to analyzing all types of plays. While prior analyses (univariate, bivariate, multivariate) have explored the link between contextual factors (e.g., match period, type of defensive marking) and the level of success of a corner kick (e.g., shot, shot on goal, goal), there has been no attempt to combine spatiotemporal event data (sequences of ball movements through the field) and contextual information to determine when and how (strategy) a particular type of corner kick play (tactic) is more likely to succeed or not. To address this gap, we propose an approach that (1) transforms spatiotemporal data into an alternative representation suitable for mining sequential patterns, (2) identifies and characterizes the sequential patterns used by offensive teams to move the ball toward the scoring zone (tactics), and (3) extracts contrast patterns to identify under what conditions different tactics result in increased chances of success or failure; we call these conditions strategies. Our results suggest that favorable and unfavorable conditions for tactic application are not the same across different tactics, supporting the argument that there is a benefit in performing an analysis that treats different tactics separately, where spatiotemporal information plays a crucial role. Unlike prior works on the corner kick, our approach can capture how the interaction between multiple contextual factors impacts the outcome of a corner kick. At the same time, the results can be explained to others in natural languages.

Urban areas can be seriously disrupted by flooding after heavy rain events. Therefore, several strategies based on grey infrastructure have been implemented over the years to mitigate the impact of significant rainfalls and make urban areas more resilient against flash floods. Green Infrastructure (GI) is an environmentally appropriate alternative which can reduce the amount of stormwater delivered to a drainage system within an urban area while mitigating the contamination carried with it. To date, however, relatively little attention has been paid to public acceptance and to the challenge GI may face in the presence of shallow aquifers. This paper presents a framework for assessing the social and technical feasibility of GI in a coastal urban area with a shallow aquifer. The method consists of the assessment of potential scenarios upon the assessment of government acceptance coupled with a stormwater management model. The urban laboratory for this study is the city of Hoboken (NJ) which is located near the estuary of the Hudson River. It was selected due to the availability of data, its vulnerability to flooding, and the presence of a shallow aquifer. Results from interviews indicate positive feedback for the implementation of GI, but specific GI techniques could not be identified. So, based on generally accepted GI measures, right-of-way, resiliency parks, and green roofs were considered and implemented into a stormwater management model. The model was used to simulate the performance of various GI options to minimize stormwater runoff. Simulation results show that all the alternatives considered are effective in reducing runoff volumes for rainfall events of less than a 1-year recurrence interval. However, they do not mitigate the negative impact of heavier rain events due to limited storage as a consequence of the size of the site and the shallow aquifer within the coastal urban area.

Although a number of recent studies on using BN for system reliability estimation have been proposed, these studies are based on the assumption that a pre‐built BN was designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. However the process of building a system‐specific BN is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a holistic method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model and estimation of the system reliability. Moreover, very limited human intervention is sufficient for the process of BN construction and reliability estimation.

This research presents a framework for analyzing the dynamics of online communities in social media platforms, utilizing a temporal fusion of text and network data. By combining text classification and dynamic social network analysis, we uncover mechanisms driving community formation and evolution, revealing the influence of real-world events. We introduced fourteen key elements based on social science theories to evaluate social media dynamics, validating our framework through a case study of Twitter data during major U.S. events in 2020. Our analysis centers on discrimination discourse, identifying sexism, racism, xenophobia, ableism, homophobia, and religious intolerance as main fragments. Results demonstrate rapid community emergence and dissolution cycles representative of discourse fragments. We reveal how real-world circumstances impact discourse dominance and how social media contributes to echo chamber formation and societal polarization. Our comprehensive approach provides insights into discourse fragmentation, opinion dynamics, and structural aspects of online communities, offering a methodology for understanding the complex interplay between online interactions and societal trends.

The recent disaster by hurricane Otis in Acapulco, Mexico, exemplifies how natural disasters, particularly hurricanes, can often arrive with a force that defies prediction despite technological advancements. Their impact, coupled with secondary disasters, underscores the urgency for efficient preemptive measures. Resilience research in the face of such calamities necessitates the creation of a safeguard before incurring irreversible losses. This research leverages big data, explicitly harnessing information obtained from social media about hurricanes, to bolster the efficacy of hurricane response efforts. The focus lies in amplifying the dissemination of adequate warnings and enhancing rescue operations. Concurrently, the research aims to delve into potential disaster rescue suggestions by tracking and analyzing high-interaction discussions on social media — X. This exploration seeks to unveil public focal points during hurricane disasters and identify valuable perspectives for future disaster response strategies. The research findings show that dynamic public discussions on X failed to adequately focus on crucial aspects such as hurricane rescue and warnings. Instead, certain discussions tended to target specific groups and individuals. By unveiling these trends, this research highlights the need to develop a nuanced understanding of interpreting and engaging with social media discussions during significant hurricane events.

The Systems Engineering Research Center (SERC) is a University Affiliated Research Center (UARC) of the US Department of Defense (DoD) formed in 2008 with more than 20 collaborator universities in the United States. Over the last decade, SERC has conducted research with Principal Investigators from universities within the SERC network, as reflected in technical reports (TR). These reports describe detailed information and analysis of the conducted research for every project under SERC support, such as written records of experiments or results of a scientific project. We analyzed the TRs from 2009 to early 2023 to identify research streams, topics, and evolution in systems engineering (SE) research using text mining and network analysis techniques, such as Louvain Community Detection and word similarity. As a result, we identified four major research streams over a decade of research projects, along with insights about topics and the evolution of SE within this time frame. Finally, we distinguished most profile authors and their most significant collaborations and networks.

Research on the performance of groups in competitive environments has traditionally focused on studying context-specific or collaboration factors without considering a multidimensional systems view integrating both. Additionally, there is limited research considering the co-dependence between the performance of a group and its adversaries. This paper proposes a framework to address these limitations by incorporating context-specific, network-based, and individual attributes to identify patterns and attributes of successful (and unsuccessful) groups. The framework provides a method to characterize performance patterns by searching for the dominant attributes that distinguish one pattern from another - relevant for decision-makers when dealing with many features. This analysis finds the different group behavior, both internal to the group and external, based on competition. The approach also identifies winning attributes through a machine-learning classification model. These factors allow differentiating a successful group and weighting context-specific network and opponent attributes. The framework is complemented with a visualization component illustrating competition with context-specific and network attributes at the player level. A case study is presented with data from FIFA World Cups in 2014 and 2018 to demonstrate the applicability of the proposed framework.

One important point of interest in urban areas is the food outlet, especially retailers that provide fresh and healthy food. Street markets, or tianguis are an affordable option throughout Mexico. Unfortunately, this type of outlet is sometimes inaccessible or significantly far to reach. This paper provides a vulnerability minimization framework to determine the optimal re-allocation of street markets by considering equity and reachability and the exact walking distance and demand by blocks in a city. The framework introduces new concepts of vulnerability along with a novel implementation of the Facility Location Problem. A case study has been used to exemplify the framework based on actual data from a region in Mexico City’s urban zone showing how significant improvements in equity and reachability can be achieved.

During the COVID-19 pandemic, most US states have taken measures of varying strength, enforcing social and physical distancing in the interest of public safety. These measures have enabled counties and states, with varying success, to slow down the propagation and mortality of the disease by matching the propagation rate to the capacity of medical facilities. However, each state's government was making its decisions based on limited information and without the benefit of being able to look retrospectively at the problem at large and to analyze the commonalities and the differences among the states and the counties across the country. We developed models connecting people's mobility, socioeconomic, and demographic factors with severity of the COVID pandemic in the US at the County level. These models can be used to inform policymakers and other stakeholders on measures to be taken during a pandemic. They also enable in-depth analysis of factors affecting the relationship between mobility and the severity of the disease. With the exception of one model, that of COVID recovery time, the resulting models accurately predict the vulnerability and severity metrics and rank the explanatory variables in the order of statistical importance. We also analyze and explain why recovery time did not allow for a good model.

This paper presents TopicRes, a five-step methodology to analyze the impact of real-life events as a function of how topics spread in online news media. Combining concepts such as text analytics, network modeling, and systems resilience, TopicRes assists in exploring and analyzing online news media spread. Data analytics and statistical tools are used in every step: data collection, topic detection, topic influence network, and topic resilience. We present a case study in the Portuguese language to showcase how TopicRes functions, its usefulness, and its versatility. The results show that the system resilience model is convenient for identifying events and capturing their dynamic behavior over time. The network deepens this analysis with detailed static snapshots of the topics and their relationships. Results are then clustered by behavior, presenting a new way to fathom the system’s dynamics enduring a certain type of disruptive event. In the case study, it is possible to observe the power dynamics of the media outlets and how the local structure influences the news spread. TopicRes is a powerful analytic tool to sense important events in the media, aid in disaster response and crisis management, track the development of new technologies, and “fake news” propagation.

The COVID‐19 pandemic presented many challenges, one of them being the imposition of “work‐at‐home” policies in March 2020. The Systems Engineering Research Center (SERC) and the International Council on Systems Engineering (INCOSE) conducted two online surveys—one during the first months of the pandemic in 2020 and the second survey 1 year after, in March 2021—to understand the impact of these policies within the systems engineering community. The surveys' format consisted of multiple‐choice questions and open‐answer questions, which were analyzed using LDA for topic modeling. Data were also collected from social media during the same timeframes to compare the feelings and experiences of systems engineers with those of the general population.

The increasingly widespread usage of the Internet and social networks has changed the way people interact with each other and react to events. These interactions enable positive collective outcomes such as enhancing collaboration in science. Conversely, undesirable effects have emerged: the self-segregation of online users within ``bubbles'' of biased content, the spread of misinformation, and the growing diffusion of aggressive speech with radical emotional valence. Affective polarization is the extent to which two opposing groups dislike one another, and it could be measured as the degree to which the two groups are willing to discriminate one against the other. Such social mechanism could occur in online social networks as a result of a controversial event in the offline world. To counter affective polarization, influential actors often make interventions using counter narratives in online social networks. However, a quantitative measure of the effectiveness of such counter narratives is typically not provided. In this study, we propose an approach to evaluate the affective polarization in online discussions generated by an offline controversial event and a measure of the effectiveness of counter narratives made by influential actors to attenuate the rise of affective polarization. The proposed approach was applied to five cases of controversial events that occurred in European soccer leagues using data collected from Twitter. Such an approach could be generalized to any other scenario involving an offline event that sparks divergent emotional reactions in online discussions and an official social media account that intervenes with a counter narrative to impact affective polarization.

Groundwater flooding (or infiltration) in sewer systems leads to significant negative consequences such as discharge of untreated sewage, reduction of system capacity, structural deterioration, and dilution of the wastewater stream delivered to a treatment plant causing malfunction. Cities with aging networks along coastal areas, where aquifers are shallow, are particularly vulnerable. Rehabilitation is necessary to mitigate the negative impact of infiltration but costly. Therefore, a prioritization strategy of intervention is required. This paper presents a decision-support model to identify the probability of infiltration into aging sewer when observations of infiltration and sewer conditions are sparse and time-limited. The model is based on logistic regression, where the variables are: material, soil, water table, and pipe size and shape. As a proof-of-concept, the method was applied to the city of Hoboken, NJ. Machine learning was used to calibrate, validate, and test the model using infiltration measurements, provided by the water authority. Upon calibration, model predictions agree well with the measurements with an accuracy of 82%. Sensitivity analysis of the model was carried out and shows that the most important parameter is the water table of the shallow aquifer. Overall, the proposed approach can be a valuable tool for strategic intervention of sewer repair and flood mitigation in urban areas.

Disasters strike communities around the world, with a reduced time-frame for warning and action leaving behind high rates of damage, mortality, and years in rebuilding efforts. For the past decade, social media has indicated a positive role in communicating before, during, and after disasters. One important question that remained un-investigated is that whether social media efficiently connect affected individuals to disaster relief agencies, and if not, how AI models can use historical data from previous disasters to facilitate information exchange between the two groups. In this study, the BERT model is first fine-tuned using historical data and then it is used to classify the tweets associated with hurricanes Dorian and Harvey based on the type of information provided; and alongside, the network between users is constructed based on the retweets and replies on Twitter. Afterwards, some network metrics are used to measure the diffusion rate of each type of disaster-motivated information. The results show that the messages by disaster eyewitnesses get the least spread while the posts by governments and media have the highest diffusion rates through the network. Additionally, the "cautions and advice" messages get the most spread among other information types while "infrastructure and utilities" and "affected individuals" messages get the least diffusion even compared with "sympathy and support". The analysis suggests that facilitating the propagation of information provided by affected individuals, using AI models, will be a valuable strategy to pursue in order to accelerate communication between affected individuals and survival groups during the disaster and aftermath.

Natural disasters affect thousands of communities every year, leaving behind human losses, billions of dollars in rebuilding efforts, and psychological affectation in survivors. How fast a community recovers from a disaster or even how well a community can mitigate risk from disasters depends on how resilient that community is. One main factor that influences communities' resilience is how a community comes together in times of need. Social cohesion is considered to be"the glue that holds society together, which can be better examined in a critical situation. There is no consensus on measuring social cohesion, but recent literature indicates that social media communications and communities play an essential role in today's disaster mitigation strategies.This research explores how to quantify social cohesion through social media outlets during disasters. The approach involves combining and implementing text processing techniques and graph network analysis to understand the relationships between nine different types of participants during hurricanes Harvey, Irma, and Maria. Visualizations are employed to illustrate these connections, their evolution before, during, and after disasters, and the degree of social cohesion throughout their timeline. The proposed measurement of social cohesion through social media networks presented in this work can provide future risk management and disaster mitigation policies. This social cohesion measure identifies the types of actors in a social network and how this network varies daily. Therefore, decisionmakers could use this measure to release strategic communication before, during, and after a disaster strikes, thus providing relevant information to people in need.