Societal Safety Research Articles

Assessing emergency physicians' competency gaps in caring for acute psychiatric emergencies: a comparative analysis of self-perceived confidence and performance against training program expectations

BackgroundThe increasing trend of patients with mental health conditions seeking emergency care is a global phenomenon. Numerous studies have emphasized the importance of emergency physicians' ability to recognize and manage psychiatric emergencies, both for patient well-being and societal safety nets. With the growing demand for emergency care related to mental health conditions, it is crucial to assess whether emergency physicians possess sufficient confidence and capabilities to adapt to these changes. Against this backdrop, our study aimed to explore the factors influencing emergency physicians' management of acute psychiatric emergencies and to critically evaluate whether current training programs adequately align with societal expectations for psychiatric emergency care.MethodsA cross-sectional survey, disseminated online by the Taiwan Society of Emergency Medicine, targeted 936 attending physicians and 449 residents in 43 Resident Review Committee approved training hospitals in winter 2021. The questionnaire covered participant demographics, skill requirements, and confidence in managing acute psychiatric patients independently. Statistical analyses included descriptive statistics, chi-square tests, medians (IQRs), and Wilcoxon rank-sum tests.ResultsOut of 229 participants, 63.8% were attending physicians, and 81.7% were affiliated with medical centers. Psychiatrists led 69.9% of emergency psychiatric training curricula, and 66.8% reported that their clinical psychiatric training outcomes were assessed by psychiatrists. Clinical seniority, training hospital level, and the average number of patients with an acute psychiatric presentation seen in a daily shift influenced physicians' self-reported confidence in managing acute psychiatric patients independently. Medical center training improved capability in managing psychiatric emergencies in the elderly, while encountering a higher average number of patients with an acute psychiatric presentation seen in a daily EM shift was associated with better performance, especially in managing specific populations.ConclusionsOur nationwide study highlights how clinical seniority, training hospital level, and patient volume per shift influence emergency physicians' confidence in managing acute psychiatric patients. However, there remains room for improvement, particularly in addressing the needs of specific patient populations. These findings underscore the importance of refining training curricula to bridge these gaps and enhance the quality of psychiatric emergency patient care.

The effects of weather factors on road traffic casualties: Analysis on provincial panel data of China from 2006 to 2021

IntroductionRoad traffic injuries stands as a major concern globally, as they result in significant loss of life, economic impact, and erode trust in government and societal safety. The influence of weather on road traffic safety is undeniable, impacting road conditions, individuals, and vehicles. However, the specific influence of weather on road traffic casualties has seldom been explored. MethodThis study assesses the effect of weather factors on road traffic casualties in China from 2006 to 2021. Vector error correction models (VECM) were utilized to determine the Granger causality between weather factors and covariates. Furthermore, panel autoregressive distribution lag models (ARDL) were applied to quantify the association between weather factors and road traffic casualties. ResultsThe findings indicate that rainfall and temperature exert a short-term negative impact on casualty risk, which intriguingly becomes positive in the long term. A standout discovery is the significant role of health investments, which are shown to reduce casualty numbers in both the short and long-terms. In the long run, the gross domestic product significantly enhances casualties, while expressway mileage notably decreases them. ConclusionsThese results demonstrate the significant influence of weather on road traffic casualties and highlight the critical roles played by factors such as gross domestic product, health investment, and expressway mileage. The evidence presented in the study underscores the urgent need for more effective strategies to mitigate road traffic casualties. Thus, some effective measures are proposed to reduce road traffic casualties. This study is conducive to the improvement of traffic in severe weather in China and provides guidance for traffic management departments.

The dorsal raphe-to-ventral hippocampal projection modulates reactive aggression through 5-HT1B receptors

Maladaptive reactive aggression is a core symptom of neuropsychiatric disorders such as schizophrenia. While uncontrolled aggression dampens societal safety, there is a limited understanding of the neural regulation involved in reactive aggression and its treatment. High levels of aggression have been linked to low serotonin (5-HT) levels. Additionally, post-weaning socially isolated (SI) mice exhibit outbursts of aggression following encountering acute stress, and hyperactivated ventral hippocampus (vHip) involves this stress-provoked escalated aggression. Here, we investigated the potential role of the raphe nucleus projecting to the vHip in modulating aggressive behavior. Chemogenetically activating the dorsal raphe nucleus (DRN) soma projecting the vHip or DRN nerve terminals in the vHip reduced reactive aggression. The reduction of attack behavior was abolished by the pretreatment of 5-HT1B receptor antagonist SB-224289. However, activating the median raphe nucleus (MRN)-to-vHip pathway ameliorated depression-like behavior but did not affect reactive aggression. DRN→vHip activation suppressed the vHip downstream area, the ventromedial hypothalamus (VMH), which is a core aggression area. Intra-vHip infusion of 5-HT1B receptor agonists (anpirtoline, CP-93129) suppressed reactive aggression and decreased c-Fos levels in the vHip neurons projecting to the VMH, suggesting an inhibition mechanism. Our findings indicate that activating the DRN projecting to the vHip is sufficient to inhibit reactive aggression in a 5-HT1B receptor-dependent manner. Thus, targeting 5-HT1B receptor could serve as a promising therapeutic approach to ameliorate symptoms of reactive aggression.

Science of Unitary Human Beings: Toward Anti-racist Actions for Human Environment Wellbecoming.

Rogers' Science of Unitary Human Beings (SUHB) and several theories that emanate from Rogers' work contain foundational concepts that may lend themselves toward nursing actions to address important social justice mandates, to advocate and to act for equity, and to uproot systems of oppression and racism in nursing. However, at the same time, theoretical concepts such as power arising from ascendant theories of SUHB are often used with little to no critical reflection for past and present-day histories of racism and power inequities in nursing and in society writ large. Using concepts related to SUHB such as integrality, turbulence, power, and patterning, we critically explore the potential of developing anti-racism reflections and actions through 3 theories: Barrett's Knowing Participation in Change; Butcher's Kaleidoscoping in Life's Turbulence; and Smith's Turbulence-Ease in the Rhythmic Flow of Patterning. We acknowledge that SUHB was/is largely developed within a framework of whiteness by scholars who were/are working from academic positions and social identities of societal safety and privilege. This requires nurses to reflect on how that history shapes SUHB. We also acknowledge the urgent need for ongoing anti-racism and justice work by nurses. As a call to action, we suggest a start by critically building upon existing theoretical foundations in SUHB to develop a more explicit anti-racist theorizing-praxis in nursing for the wellbecoming of humans and nonhumans alike.

Deep learning-based corrosion inspection of long-span bridges with BIM integration

Infrastructure operation and maintenance is essential for societal safety, particularly in Japan where the aging of infrastructures built during the period of high economic growth is advancing. However, there are issues such as a shortage of engineers and inefficiencies in work, requiring improvements in efficiency and automation for their resolution. Nevertheless, there are still many inefficiencies in the current procedures for bridge inspections. Usually, inspection engineers check for damage on bridges through close visual inspections at the site, then photograph the damaged parts, measure the size by touch, and create a report. A three-dimensional representation, considering the front and back of the structural elements, is needed for identifying damage, necessitating the creation of multi-directional three-dimensional drawings. However, this process is labor-intensive and prone to errors. Furthermore, due to the lack of uniformity in records, it is challenging to refer to past inspection histories. Especially for long bridges, without resolving such issues, the required labor and the number of mistakes could exceed acceptable limits, making proper management difficult. Therefore, in this study, we developed a method for automatically measuring the position and area of corroded parts by capturing images of the lower surface of the stiffening girder using a bridge inspection vehicle and utilizing image diagnosis technology. By integrating these results into a 3D model called BIM (Building Information Modeling), it becomes possible to manage the bridge more efficiently. We verified this method on actual long bridges and confirmed its effectiveness.

Vegetation increases global climate vulnerability risk by shifting climate zones in response to rising atmospheric CO2

Global climate zones are experiencing widespread shifts with ongoing rise in atmospheric CO2, influencing vegetation growth and shifting its distributions to challenge ecosystem structure and function, posing threats on ecological and societal safety. However, how rising atmospheric CO2 affects the pace of global climate zone shifts is highly uncertain. More attentions are urgently required to understand the underlying mechanisms and quantifications of regional climate vulnerability in response to rising CO2. In this study, we employ nine Earth system models from CMIP6 to investigate global climate zone shifts with rising CO2, unravel the effects of vegetation physiological response (PHY), and categorize climate vulnerable regions depending on the extent of climate zone shifts. We find that climate zone shifts over half of the global land area, 16.8% of which is contributed by PHY at 4 × CO2. Intriguingly, besides warming, PHY-induced precipitation changes and their interactions with warming dominate about two-fifths of PHY-forced shifts, providing potential direction for model improvement in future predictions of climate zone shifts. Aided with PHY effects, 4 × CO2 imposes substantial climate zone shifts over about one-fifth of the global land area, suggesting substantial changes in local climate and ecosystem structure and functions. Hence, those regions would experience strong climate vulnerability, and face high risk of climate extremes, water scarcity and food production. Our results quantitatively identify the vulnerable regions and unravel the underlying drivers, providing scientific insights to prioritize conservation and restoration efforts to ensure ecological and social safety globally.

CHALLENGES IN WORK LIFE BALANCE OF UNORGANISED SECTOR: AN EXPLORATORY STUDY

This research study explores the fundamentals that influence the imbalance among work and personal life for persons who are working in the unorganized sector. It explicitly emphases on the challenges impersonated by irregular working hours, uncertainty of income, and lack of societal security. The unorganized segment, considered by casual engagement and negligible rules, nowadays characteristics complications that make impacts on employees ability to balance their work and personal life obligations. Through a mixed method research study examines into the acquaintances concerning the elements and work-life balance, exploring significant differences based on gender and occupation prominence. The analysis reveals that irregular working hour and monetary volatility considerably contribute to inadequate of work-life balance, specifically impacting to the women and workers with low incomes. This research focuses on the significance policies to improve working circumstances and spread societal safety treatment, which are necessary for progress of unorganised labours and instituting a further impartial of work-life balance

Real-Time Classification Model of Public Emergencies Using Fusion Expansion Network

In today's deep learning-dominated era, real-time classification of public emergencies is a critical research area. Existing methods, however, often fall short in considering both temporal and spatial aspects comprehensively. This study introduces GEDNAS, a novel model that combines atrous convolutional neural network (DCNN), gated recurrent unit (GRU), and neural structure search (NAS) to address these limitations. GEDNAS utilizes DCNN to capture local spatio-temporal features, integrates GRU for time series modeling, and employs NAS for overall structural optimization. The approach significantly enhances real-time public emergency classification performance, showcasing its efficiency and accuracy in responding to real-time scenarios and providing robust support for emergency response efforts. This research introduces an innovative solution for public safety, advancing the application of deep learning in emergency management and inspiring the design of real-time classification models, ultimately enhancing overall societal safety.

The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data.

Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach. The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods. This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods.

Empowering Women Employees’ Safety through Artificial Intelligence – An Empirical Study

Now a day it is very common human collaboration with machine. Artificial Intelligence, Internet of Things and Business Analytics are the important terms in technology. Top business companies through the world have started concentrating on Artificial Intelligence and machines. In present era, machines have started to act like human. Artificial intelligence has been adopted in various sectors since this technology, improves productivity, decreases costs, and quickly resolves challenging issues. It has been predicted that one of the most promising technologies of the future will be artificial intelligence. As one of the latest modern technologies, AI is influencing developments throughout a number of industries and societal safety norms. In this paper has an attempt to know women employee safety level through Artificial Intelligence and to identify the factors which are influencing for women empowerment safety through AI. An online survey was conducted and circulated through emails to collected the data, to study the status and level of empowerment among women safety through Artificial Intelligence. The status of women empowerment was evaluated using questionnaire. The information like age, educational qualification, occupation, marital status, work experience, city of residence was collected and analysed with five-point Likert scale. The questionnaire was administrated and circulated on a sample of 120 women associated with various email groups. The questionnaire was response rate was 42%, as 50 women respondents retuned fully filled, usable questionnaire. Data collected for this study were evaluated using Multiple Regression and ANOVA in SPSS statistical package. Empowering women employees’ safety through AI involved a combination of technology, regulations, policies and a supportive working environment. It is very essential to implement AI systems that respect privacy and maintain ethical standards while enhancing safety

A framework for business continuity management in a multiactor context

AbstractBusiness continuity management (BCM) is an analytic process capable of fostering the resilience of critical societal functions. While previous research on BCM primarily has focused on its applications within individual organizations, today's modern society involves networks of interdependent actors collectively delivering these essential functions to end users. This necessitates an expansion of BCM to accommodate a multiactor context. Critical considerations that need to be addressed in multiactor BCM involve shared use of a common, finite resource by several actors, or situations where an alternative continuity solution is relied upon by more than one actor, but its capacity is insufficient for simultaneous use by all. The aim of this paper is to propose a framework for multiactor BCM. The framework expands upon existing ISO 22301 standards to outline how BCM can be approached for the multiactor context. It diverges from the conventional BCM approach conducted by individual actors and embraces a societal safety perspective, facilitating the definition of a common analytic focus and promoting high conceptual harmonization. While acknowledging potential practical barriers, the framework facilitates the connection of BCM efforts from different actors with minimal additional analytic efforts. The overarching goal of the framework is to provide increased understanding of the continuity of the multiactor system, specifically focusing on its ability to deliver the most critical products or services to end users. Based on this understanding, the aim is to identify ways to further improve the continuity, for example by identifying and ranking critical products or services, identifying resources or alternative continuity solutions that many actors depend on in delivering their critical products and services. Finally, the paper addresses lingering challenges and highlights success factors necessary for the effective application of the framework.

Advancements in Satellite Remote Sensing for Predicting Large-Scale Wildfire Risks: An Image Processing Algorithmic Framework

With the escalation of global warming and human activities, large-scale wildfires have become increasingly frequent, posing significant threats to both ecological environments and human societal safety. Satellite remote sensing technology plays a pivotal role in wildfire monitoring and risk assessment, providing extensive geographical coverage and continuous monitoring capabilities. Traditional methods for predicating wildfire risk, however, face limitations in processing large-scale remote sensing data, especially in cloud detection and temporal information analysis. In response to this challenge, a novel set of image processing algorithms has been developed to enhance the efficiency and accuracy of wildfire risk prediction. Initially, a cloud detection and removal algorithm based on deep learning is introduced. This algorithm effectively identifies and eliminates cloud interference in remote sensing images, thereby significantly improving the quality and usability of image data. Subsequently, a temporal information capturing technique is proposed, capable of processing vast amounts of remote sensing data and extracting time series features. This technique provides robust data support for wildfire risk prediction. The application of these technologies not only improves the efficiency of the data processing workflow but also enhances the timeliness and accuracy of the prediction model, holding significant practical importance for guiding actual wildfire prevention and response measures.

Increased impact sensitivity in ageing high explosives; analysis of Amatol extracted from explosive remnants of war.

Millions of tonnes of explosive remnants of war remain in nature and their volume is continuously growing. The explosive legacy of wars represents an increasing threat to the environment and societal safety and security. As munitions continue to deteriorate, harmful constituents will eventually leak into the environment, poisoning ecological receptors and contaminating the surrounding soil and groundwater. Moreover, munition deterioration due to exposure to various environmental factors may ultimately cause them to become increasingly sensitive to external stimuli and susceptible to accidental detonation. To thoroughly assess how to address these ageing munitions, we must first establish certain threshold values for safe and secure handling and final disposal of the explosive ordnance. One key factor is to establish how the impact sensitivity of the explosives evolves over time. In the present work, we investigated the high-explosive substance Amatol extracted from ageing explosive remnants of war. The results obtained in the analysis indicate that the high explosives in the examined specimens were generally much more sensitive to impact than previously assumed. Furthermore, the analysis revealed that the standardized methodology of impact sensitivity testing was insufficient for estimating the sensitivities in question, and a more careful statistical analysis is required.

The Dynamic Between Economic Factors and Violent Crime Rates: A Comparative Analysis of Global Trends and Legislations

This paper explores the relationship between macroeconomic factors and crime rates, focusing on case studies. It examines the question: to what extent do macroeconomic factors influence global violent crime rates, and how do legislative interventions affect this dynamic? The methodology employs quantitative analysis of various economic indicators alongside qualitative case studies of the United States of America, Japan, Malaysia, and Romania. Findings suggest a significant impact of economic factors on crime rates. The dynamic between unemployment and inflation is crucial to decoding general trends; however, other factors such as region, inequality, and poverty also affect crime rates. While economic variables play a role, other social, cultural, and legislative factors also contribute to crime dynamics. The cohesion between economic stability and legislative interventions emerges as a crucial determinant in shaping societal safety and well-being.

Smart city design plan selection through single-valued neutrosophic probabilistic hesitant fuzzy rough aggregation information

One of the hottest areas for applying the solutions currently available is the internet of things-based smart housing society architecture and its uncertainty analysis. When intelligent parking, waste management, public transportation, public safety, and other automatic methods for housing society’s growth were implemented, it became even more crucial. An intelligent, smart system is necessary to manage these problems and provide smooth services. Additionally, it will be helpful in reducing issues with time waste and societal safety. However, the issue comes up when describing accurate, approximate, or questionable parking, transit, safety, and waste management areas. This paper discusses several mathematical solutions for the smart housing society that use fuzzy rough sets, probabilistic hesitant fuzzy sets, and their extensions with neutrosophic sets. For further growth, a few studies on the graphic display of the evolution of the smart housing society are also considered. The rough set theory can be useful when dealing with imprecise, incomplete, or indeterminate data sets. The core contribution of this work is the construction of a novel generalized notion of a single-valued neutrosophic probabilistic hesitant fuzzy rough set (SV-NPHFRS), which is a hybrid structure of the single-valued neutrosophic set, the probabilistic hesitant fuzzy set, and the rough set. In contrast to the present literature, the underlying idea of SV-NPHFRS is that it is a powerful mathematical tool for managing uncertainty and imperfect information. This method is particularly beneficial when there are a number of competing criteria to consider. The aggregation technique plays an important role in decision-making concerns, especially when more competing criteria are present. In the study’s comparison phase, the suggested decision support system is compared to relevant existing approaches. The results suggest that, in terms of choice flexibility, the suggested technique has the potential to outperform the drawbacks of the current decision-making tools. The proposed study is expected to be useful for a number of researchers conducting future work on housing societies, waste management, public safety diagnostics, and hybridization.

Risk science applied to major risk events in history.

Major risk events in history are often labeled as black swans or as unforeseeable given the risk policies and procedures existing at the time. Hindsight suggests that many of these events could have been foreseeable. This article explores past risk events, (1) analyzes how risk science principles apply to those events, and (2) studies gaps and opportunities for risk science using the lenses of consequences, uncertainty, and knowledge as they relate to evidence used for risk assessment prior to the risk event. New insights are obtained, relating to general foundational risk science issues and a classification system for characterizing the integrity and quality of evidence in risk studies. The analysis results are used to identify how risk science approaches contribute to the overall management of risk and societal safety, and where improvements can be obtained.

A Dog Accompanying a Man Makes Social Threatening Contexts Less Aversive and Enhances Perceived Safety Regardless of Societal Safety Levels

ABSTRACT Research shows that the inclusion of a dog in pictures showing a man in threatening scenarios improves women’s emotional reactions to them. However, the magnitude of this “dog-accompanying effect” (i.e., whether the inclusion of a dog renders socially threatening scenes less aversive, neutral, or positive) in societies differing in terms of safety remains unknown. To address this, undergraduate women from societies differing in safety levels (lower [Colombia, n = 120] and higher [Spain, n = 131] safety levels) provided valence, arousal, dominance, and safety ratings to images depicting a man alone or with a dog in threatening scenes, as well as to images of pleasant and neutral social scenes. The same response pattern was found in both countries: when viewing images of a dog accompanying a man, women gave lower valence, dominance, safety, and calmness ratings than when viewing images of neutral scenes. Conversely, women gave higher valence, dominance, safety, and calmness ratings when observing images of a dog accompanying a man compared with images showing the man alone in threatening scenes. Overall, these data indicate that a dog’s presence in pictures buffered negative emotional reactions to photos of threatening social scenes in societal contexts differing in safety levels, but still (slightly) activated the defensive motivational system. The slight activation of the defensive motivational system is interpreted as an adaptative response of the person to reduce or avoid potential harm (e.g., a hypothetical attack by the man). Our study provides a deeper understanding of the influence of dogs on women’s emotional reactions to images of threatening scenes by clarifying the magnitude of the dog effect in societies differing in safety levels.

Future sea level rise exacerbates compound floods induced by rainstorm and storm tide during super typhoon events: A case study from Zhuhai, China

Compound floods are becoming a growing threat in coastal cities against a background of global sea level rise (SLR), and may cause increasing impacts on societal safety and economy. How to quantify the impact of SLR and compound effects among various flood causes on compound flood have become important challenges. We propose a modeling framework which integrates atmospheric, storm tide and urban flood (IASTUF) models to characterize the various physical processes related to compound flood. Future SLR projections under various shared socioeconomic and respective concentration pathway emission scenarios are considered. Hengqin Island (Zhuhai City, China) frequently experiences typhoon conditions combined with rainstorm and storm surge events. Its population has increased more than sixfold during the past decade, stimulating urgent demands for assessments of the potential risks associated with future compound floods in the context of potential SLR. A compound flood event in northern Hengqin Island, caused by the super typhoon Mangkhut in 2018, is selected as a case study to verify the proposed modeling framework. Results show that the IASTUF modeling framework can capture well the combined processes of typhoon, rainstorm, storm tide and inland flooding and demonstrates good performance in quantifying compound flood magnitudes. Compared to the current scenario, the node flooding volume (from the drainage system) and the maximum inundation area (with inundation depths >1 m) in 2050 are projected to increase by 20–26 % and 41–85 %, respectively, and these increases rise to 46–84 % and 23–71 times by 2100. The inundation volumes and water depths due to compound events are larger than the sum of those caused by the corresponding single-cause events, indicating that concurrent rainstorm and storm surge induce positive compound effects on flood magnitude. These findings can provide guidance for the management and mitigation of future compound flood hazards driven by super typhoon events.

Online conspiracy communities are more resilient to deplatforming.

Online social media foster the creation of active communities around shared narratives. Such communities may turn into incubators for conspiracy theories-some spreading violent messages that could sharpen the debate and potentially harm society. To face these phenomena, most social media platforms implemented moderation policies, ranging from posting warning labels up to deplatforming, i.e. permanently banning users. Assessing the effectiveness of content moderation is crucial for balancing societal safety while preserving the right to free speech. In this article, we compare the shift in behavior of users affected by the ban of two large communities on Reddit, GreatAwakening and FatPeopleHate, which were dedicated to spreading the QAnon conspiracy and body-shaming individuals, respectively. Following the ban, both communities partially migrated to Voat, an unmoderated Reddit clone. We estimate how many users migrate, finding that users in the conspiracy community are much more likely to leave Reddit altogether and join Voat. Then, we quantify the behavioral shift within Reddit and across Reddit and Voat by matching common users. While in general the activity of users is lower on the new platform, GreatAwakening users who decided to completely leave Reddit maintain a similar level of activity on Voat. Toxicity strongly increases on Voat in both communities. Finally, conspiracy users migrating from Reddit tend to recreate their previous social network on Voat. Our findings suggest that banning conspiracy communities hosting violent content should be carefully designed, as these communities may be more resilient to deplatforming.

An interconnectivity framework for analyzing and demarcating real-time operations across critical infrastructures and over time

Models and modeling of interconnected critical infrastructures are on the rise. A different framework is provided here for analyzing and distinguishing interconnected operations in real time and over time. Our framework treats interconnected operations as the unit and level of analysis rather than as methodologically distinct operations of conceptually separate infrastructures. We focus on interconnectivity configurations, shifts, connected system control variables, and changing performance standards across important shifts. Implications of the conceptual and methodological refocusing are drawn for policy and management. Our framework allows for a new understanding of reliability in relation to interconnectivity management, including the identification of potential errors in assessing and acting upon interconnectivity shifts. These include failing to recognize when changes in communication patterns need to follow from interconnectivity shifts and neglecting the operational importance of improvisations where system control variables (like electricity frequency or water pressure) overlap or are shared. Larger implications for interinfrastructural and societal safety are drawn throughout.