Robust Resources Research Articles

Spatio-Temporal Distribution and Spatial Spillover Effects of Net Carbon Emissions: A Case Study of Shaanxi Province, China

Scientifically evaluating net carbon dioxide (CO2) emissions is the pivotal strategy for mitigating global climate change and fostering sustainable urban development. Shaanxi Province is situated in central China, and boasts robust energy resources in the north and a significant carbon-sink zone in the southern Qinling Mountains. Therefore, uncovering the spatial distributions of net CO2 emissions and identifying its influencing factors across cities in Shaanxi Province would furnish a crucial theoretical foundation for advancing low-carbon development strategies. In this research, the net CO2 emissions of cities in Shaanxi Province from 2005 to 2020 are calculated using the carbon-emission-factor calculation model, then the Geodetector is utilized to evaluate the single-factor explanatory power and two-factor interactions among the fourteen various influencing variables, and then the spatial econometric model is employed to analyze the spatial spillover effects of these key factors. The results show the following: (1) The net CO2 emissions present significant regional differences among the ten cities of Shaanxi Province, notably Xi’an City, Yulin City, and Weinan City, which have recorded remarkable contributions with the respective totals reaching 72.2593 million tons, 76.3031 million tons, and 58.1646 million tons. (2) Regarding temporal trend changes, the aggregate net CO2 emissions across whole province underwent a marked expansion from 2005 to 2019. Yulin City and Shangluo City exhibit remarkable surges, with respective average annual growth rates soaring at 7.38% and 7.39%. (3) From the perspective of influencing factors, GDP exhibits the most pronounced correlation spanning the entire province. Meanwhile, foreign investment emerges as a significant contributor specifically in Xi’an and Yulin City. Moreover, interaction detection reveals most factor combinations exhibit bi-enhancement, while a few exhibits intricate and non-linear enhancement. (4) The SDM regression and fixed-effect analysis reveal that city GDP had a positive spillover effect on neighboring cities’ net CO2 emission, while investment in scientific research and technology services, along with per capita construction land, exhibit notable negative spillovers, suggesting potential emission reduction benefits across cities.

Which Machine Learning Algorithm Is Best Suited for Estimating Reference Evapotranspiration in Humid Subtropical Climate?

ABSTRACTTimely and reliable estimates of reference evapotranspiration (ET0) are imperative for robust water resources planning and management. Applying machine learning (ML) algorithms for estimating ET0 has been evolving, and their applicability in different sectors is still a compelling field of research. In this study, four Gaussian process regression (GPR) algorithms—polynomial kernel (PK), polynomial universal function kernel (PUK), normalized poly kernel (NPK), and radial basis function (RBF)—were compared against widely used random forest (RF) and a simpler locally weighted linear regression (LWLR) algorithm at a humid subtropical region in India. The sensitivity analysis of the input variables was followed by application of the best combination of variables in algorithm testing and training for generating ET0. The results were then compared against the Penman–Monteith method at both daily and monthly time steps. The results indicated that ET0 is least sensitive to wind speed at 2 m height. Additionally, at a daily time step, RF, followed by PUK, generated the best results during both training and testing phases. In contrast, at a monthly time step, using multiple model evaluation matrices, PUK followed by RF performed best. These results demonstrate the application of the ML algorithms is subjected to user‐required time steps. Although this study focused on Northwest India, the findings are relevant to all humid subtropical regions across the world.

Assess Attitude and Knowledge Regarding Menopause and Its Management among Rural Women between the Age Group of 45-50 Years

This study explored the attitudes and knowledge of rural women aged 45-50 regarding menopause and its management, focusing on how healthcare access and social support impact these perspectives. Using purposive sampling and structured questionnaires, data were collected from participants to assess their understanding and experiences with menopause. Results indicated that 58.41% of the women had a positive attitude towards menopause, while 41.62% held a negative view. Nearly half of the participants (47.75%) possessed adequate knowledge about menopause, with 52.25% lacking sufficient understanding. Healthcare access was available to 84.57% of the participants, suggesting that most women had access to medical resources, though only 22.73% felt they had a strong support network for discussing menopause-related issues. These findings highlight a need for more robust educational resources and social support structures to improve knowledge and foster positive attitudes toward menopause management among rural women.

‘Future Docs for Abortion Access’: lessons learnt from 1 year of medical student advocacy in the USA

Abortion’s place in US medical education has long been tenuous. Most medical students have historically lacked adequate abortion instruction, which stands to worsen following the Dobbs v. Jackson Women’s Health...

Challenges and opportunities of developing bioinformatics platforms in Africa: the case of BurkinaBioinfo at Joseph Ki-Zerbo University, Burkina Faso.

Bioinformatics, an interdisciplinary field combining biology and computer science, enables meaningful information to be extracted from complex biological data. The exponential growth of biological data, driven by high-throughput omics technologies and advanced sequencing methods, requires robust computational resources. Worldwide, bioinformatics skills and computational clusters are essential for managing and analysing large-scale biological datasets across health, agriculture, and environmental science, which are crucial for the African continent. In Burkina Faso, the establishment of bioinformatics infrastructure has been a gradual process. Initial training initiatives between 2015-2016, including bioinformatics courses and the establishment of the BurkinaBioinfo (BBi) platform, marked significant progress. Over 250 scientists have been trained at diverse levels in bioinformatics, 105 user accounts have been created for high-performance computing access. Operational since 2019, this platform has significantly facilitated training programs for scientists and system administrators in west Africa, covering data production, introductory bioinformatics, phylogenetic analysis, and metagenomics. Financial and technical support from various sources has facilitated the rapid development of the platform to meet the growing need for bioinformatics analysis, particularly in conjunction with local 'wet labs'. Establishing a bioinformatics cluster in Burkina Faso involved identifying the needs of researchers, selecting appropriate hardware and installing the necessary bioinformatics tools. At present, the main challenges for the BBi platform include ongoing staff training in bioinformatics skills and high-level IT infrastructure management in the face of growing infrastructure demands. Despite these challenges, the establishment of a bioinformatics platform in Burkina Faso offers significant opportunities for scientific research and economic development in the country.

Container migration for edge computing in industrial Internet with joint latency reduction and reliability enhancement

Edge computing has emerged as a prominent trend in the field of information technology, offering flexible and robust resources for the industrial Internet. How to migrate container accurately is crucial for edge computing in the industrial Internet, as it plays a vital role in enhancing service response speed and safeguarding uninterrupted continuity of production operations. In this paper, we explore the problem of container migration in edge computing within the industrial Internet, aiming to reduce latency and enhance reliability. We establish a two-objective optimization model to comprehensively capture the container migration problem and formulate it as a constrained optimization model. The formulated model provides a systematic framework that effectively balances the trade-off between reducing latency and enhancing reliability. To tackle the migration strategy derived from the optimization model, we propose a migration algorithm based on the improved binary whale optimization algorithm. Our migration algorithm incorporates the adaptive probability and adaptive position weight within the hunting and searching operations, effectively enhancing the search efficiency during the solving process. The experimental results demonstrate the effectiveness of the established model in reducing the objective value, while the proposed migration algorithm surpasses existing algorithms by achieving an average reduction of at least 15.59% in the objective value.

A traditional ecological knowledge summit

A traditional ecological knowledge summit The Global Center for Climate Change and Transboundary Waters (GCTW) cohosts a Traditional Ecological Knowledge Summit (1), as Gail Krantzberg (2), Peter Czajkowski, Dawn Martin-Hill, Rohini Patel, Hiliary Monteith, and Drew Gronewold explain. The Global Center for Climate Change and Transboundary Waters (GCTW) integrates hydroclimate modeling, water quality forecasting, and community-engaged mixed methods that harmonize and propagate Traditional Ecological Knowledge (TEK), Indigenous Knowledge (IK), and Western Science (WS) into robust 21st-century transboundary water resources governance protocols. The U.S. National Science Foundation and the Canadian Social Science and Humanities Research Council fund the Center. It supports a multinational network of researchers that is designed to promote information sharing across borders.

Robust joint multiple resources allocation algorithm for cooperative underwater acoustic communication networks

AbstractIn this article, a joint relay selection, power control and time allocation problem is studied to maximize the energy efficiency for the cooperative underwater acoustic communication networks. The joint optimization problem is full of challenges due to the strong coupling of multiple resources and the uncertain characteristics of the underwater acoustic communication scenario. To address this issue, the worst‐case method is employed to transform an original uncertain problem into a deterministic problem. Furthermore, we propose the block coordinate descent‐based method to decouple the strongly coupling multi‐resource allocation problem into three relatively independent sub‐problems. The coupling of multiple resources is completely decoupled, thereby greatly reducing the solving difficulty. In addition, given that the sub‐problems with the fractional objective function are still non‐convex and hard to solve, the Dinkelbach‐based method is proposed to transform the fractional objective function into a subtractive form. At last, the relay selection sub‐problem is transformed into a integer programming problem, and the time allocation sub‐problem is transformed into a linear programming problem, whose optimal solutions can be obtained by some well‐established solution methods. The power allocation problem is transformed into a convex optimization problem, which can be solved by the Lagrangian dual method. Finally, in the proposed iteration structure, the three sub‐problems are alternatingly solved until convergence. Simulation results are presented to demonstrate the efficiency and robustness of the proposed algorithm.

IoT and Smart Sensors for Remote Sensing Healthcare and Agriculture Applications

In order to better serve patients and make better decisions, the healthcare industry is always adapting to incorporate new technology. In this in-depth analysis, we look at how geographic information systems (GIS) and remote sensing (RS) have revolutionised contemporary medicine. Improving healthcare planning, illness management, and environmental health evaluation are just a few of the many potential outcomes of integrating remote sensing and GIS, two technologies that can gather data from great distances and then analyze it spatially. We explore the many facets of SR and GIS in health care in this paper. To guarantee the inclusion and relevance of the literature examined, we explain the methodology utilised for data collection, the criteria for articles and studies to be selected, and the strategic search strategy that was followed. The essay starts out by providing a basic overview of healthcare remote sensing. We delve into its significance by going over the wide range of uses, which include tracking diseases, evaluating the state of the environment, and dealing with disasters. These uses case studies illustrate how remote sensing has changed healthcare and how public health dynamics might be better understood. This article delves into the ways in which healthcare accessibility can be analyzed spatially using GIS and remote sensing. In order to plan for health effectively and provide services fairly, it is crucial to understand the distribution and accessibility of health facilities across different geographic areas. This is because access to health services is a cornerstone of public health. The combination of geographic information systems (GIS) with remote sensing technology provides robust resources for assessing and bettering healthcare accessibility. Examining these technologies’ theoretical foundations, methodology, and practical uses in healthcare affordability analysis is the focus of this article.

Mental health challenges within the LGBTQ community: A societal imperative.

The LGBTQ community faces specific stressors rooted in societal discrimination, bias, and marginalization, impacting mental health significantly. Persistent discrimination, exclusion, and heteronormative expectations are identified as social determinants that contribute to heightened stress levels, resulting in increased rates of anxiety, depression, and suicidality. Moreover, the COVID-19 pandemic has also severely affected the physical and mental health of a large percentage of the population, particularly impacting the health of marginalized individuals such as LGBT individuals. Studies consistently reveal alarming mental health disparities between LGBTQ individuals and their heterosexual counterparts, with elevated rates of mood disorders, substance abuse, and self-harm. Transgender individuals, in particular, face unique challenges, including high rates of gender dysphoria and discrimination-induced mental distress. The concept of intersectionality within the LGBTQ community emphasizes the compounded burden faced by individuals with multiple marginalized identities, necessitating tailored mental health strategies. The societal context, including legal and policy frameworks, plays a pivotal role in shaping the mental health and scope for LGBTQ individuals. Supportive frameworks, such as anti-discrimination laws, marriage equality, and healthcare policies, contribute to societal acceptance and individual well-being. Additionally, efforts to destigmatize LGBTQ identities, comprehensive sex education, and public awareness campaigns challenging stereotypes are crucial for fostering a more inclusive societal mindset. Establishing robust support networks within the LGBTQ community, including initiatives providing mental health resources, counseling, community spaces, and allyship programs, is vital for community resilience. These efforts not only contribute to the well-being of individuals within the LGBTQ community but also work toward creating a more inclusive society. In conclusion, addressing mental health challenges within the LGBTQ community requires a multifaceted approach involving societal understanding, legal support, destigmatization efforts, and robust community resources. By acknowledging and comprehending the unique struggles faced by the LGBTQ community, society can collectively work toward building a more compassionate, supportive, and inclusive environment for all individuals, irrespective of their sexual orientation or gender identity.

Navigating the Integration of Machine Learning in Healthcare: Challenges, Strategies, and Ethical Considerations

The amalgamation of artificial intelligence (AI) and machine learning (ML) in healthcare offers a revolutionary prospect to improve patient outcomes, optimize workflow, and curtail expenses. Robust computational resources, competent interdisciplinary teams, and careful management of high-quality, diverse datasets are necessary for the successful deployment of ML models. To preserve public confidence and guarantee adherence to legal requirements, ethical issues, specifically those pertaining to patient consent, data privacy, and confidentiality are crucial. Regulatory bodies, legislators, and healthcare providers must all be involved in stakeholder engagement to establish a supportive environment for ML integration. The research highlights the significance of transparency and interpretability, promoting explainable AI models that augment clinician trust and enable wider adoption in the medical community. This thorough analysis finds important gaps in the current state of ML applications in healthcare and highlights new trends. It discusses ethical ramifications, realistic implementation guidelines, and the need for cooperative stakeholder engagement. The goal of the paper is to close the “translation gap” between model development and clinical application by examining real-world applications and offering a framework for the methodical integration of ML in clinical settings. The study promotes models that are not only technically sound but also user-friendly and in line with the practical needs of healthcare professionals by emphasizing the human-centric design of ML systems. By facilitating accurate and timely diagnoses, optimizing treatment plans, and eventually raising the standard of healthcare services as a whole, the strategic application of ML in the medical field holds the potential to completely transform patient care. This study opens the door to a future in which AI-driven healthcare solutions are smoothly integrated into routine clinical practice by providing useful insights and guidelines to enhance trustworthy and moral integration.

Robust multi-objective optimization under multiple uncertainties using the CM-ROPAR approach: case study of water resources allocation in the Huaihe River basin

Abstract. Water resources managers need to make decisions in a constantly changing environment because the data relating to water resources are uncertain and imprecise. The Robust Optimization and Probabilistic Analysis of Robustness (ROPAR) algorithm is a well-suited tool for dealing with uncertainty. Still, the failure to consider multiple uncertainties and multi-objective robustness hinders the application of the ROPAR algorithm to practical problems. This paper proposes a robust optimization and robustness probabilistic analysis method that considers numerous uncertainties and multi-objective robustness for robust water resources allocation under uncertainty. The copula function is introduced for analyzing the probabilities of different scenarios. The robustness with respect to the two objective functions is analyzed separately, and the Pareto frontier of robustness is generated. The relationship between the robustness with respect to the two objective functions is used to evaluate water resources management strategies. Use of the method is illustrated in a case study of water resources allocation in the Huaihe River basin. The results demonstrate that the method opens a possibility for water managers to make more informed uncertainty-aware decisions.

What about the “soft factors”? Longitudinal effects of leadership behaviors on psychological resources of firefighters

In addition to adequate training and reliable equipment, firefighters need robust psychological resources (e.g., self-efficacy, team cohesion) to sustain readiness for potential deployment in large-scale disasters. However, recurrent exposure to emergency situations in their every-day work poses a threat to psychological resources and jeopardizes readiness. In this study, we examine how leaders in fire services support their subordinates in maintaining high levels of these resources. We hypothesize that person-oriented leadership is particularly effective in this respect. Specifically, we predict that person-oriented leader behaviors increase subordinates' psychological resources and magnify the benefits of their performance behaviors in terms of resource acquisition. In a preregistered longitudinal study (five monthly measurements) with firefighters in Germany (N = 189), we tested our hypotheses using random intercept cross-lagged panel modeling. In line with our expectations, perceived person-oriented leader behaviors predicted subordinates' resources on a month-to-month basis, and also in an additional follow-up survey one year later. However, person-oriented leadership did not increase the return on subordinates' performance behavior in terms of resources. Notably, perceived task-oriented leader behaviors also predicted subordinates’ resources, but only on a month-to-month basis. We discuss possible mechanisms and time frames of the observed effects. Together, our findings indicate that person-oriented leadership can counteract resource depletion due to adverse working conditions and, thus, add to higher levels of readiness. Policymakers and senior executives may foster this effect by integrating person-oriented competencies into leadership norms and training programs.

Whole-Genome Sequence Resources for Pseudomonas amygdali pv. sesami and Xanthomonas arboricola Isolated from Sesame (Sesamum indicum) in North Carolina in 2022

Sesame ( Sesamum indicum) is a specialty crop with increasing interest as an alternative, rotational crop for U.S. agricultural systems. This crop is susceptible to several pathogens, including Pseudomonas and Xanthomonas species. Two bacterial isolates were recently cultured from sesame in North Carolina symptomatic of bacterial spot and identified as Pseudomonas amygdali pv. sesami (Pas) and Xanthomonas sp. These isolates were used for hybrid whole-genome sequencing and assembly using Illumina and PacBio to develop robust genomic resources for these disease-causing strains. The results provide the most complete Pas genome available and placed the Xanthomonas sp. isolate into the X. arboricola species designation. Improved genomic resources for pathogens of sesame are needed to accurately detect, characterize, and employ timely management of the disease. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Resource Control in Active IRS-Aided 6G IoT Networks with Use Case in Smart Indoor Communication

The IoT ecosystem involves connected smart devices that support massive amount of data for processing and further analysis. The proliferating massive IoT network demands network connectivity, robust communication links and computational resources which puts huge load on the network. To overcome the communication overhead in the IoT landscape while offering optimal fault tolerance is the main challenge. This paper presents an intelligent solution for improving the communication efficiency of IoT network using active IRS technology. An active IRS aided communication framework is proposed which offers enhanced network connectivity by enabling controlled reflection amplitude. An algorithm is proposed which associates optimal active IRS to each AP-node link such that signal-to-interference-plus-noise ratio (SINR) is maximized. It is observed that the proposed association scheme in active IRS system offers an improvement of 19.04% in achievable rate over random association at AP transmit power [Formula: see text] of 20[Formula: see text]dBm and IRS amplification power [Formula: see text] of 4[Formula: see text]dBm. Furthermore, the system outage probability is carried out with change in [Formula: see text] and amplification gain [Formula: see text]. The mean channel power is also evaluated for different IRS densities and AP densities under different IRS reflecting elements [Formula: see text], [Formula: see text] and [Formula: see text]. In the end, the performance comparison with passive IRS system and system with no IRS assistance is carried out. A use case scenario of active IRS-aided system in smart indoor communication is also discussed.

Business robot farm management in the Robotic Process Automation (RPA)

The growing demand for innovative IT solutions allowed the existing industries to develop and enabled the emergence of new ones, which filled the existing technological gaps. Among them, we could observe a dynamic growth of the robotic process automation sector. Business robots are designed to automate the organization’s business processes by imitating real users performing their daily duties, which are based on structured, repetitive procedures, increasing the operational efficiency of the organization while reducing the impact of the human factor. With the advent of this solution, there were also issues regarding the management model of the RPA robot farm. The results presented in this article are based on the results obtained from a case study on an organization operating in the IT consulting sector. This article addresses critical challenges in managing a robotic process automation infrastructure, focusing on financial optimization, knowledge management, benefits evaluation, and security enhancement. Through the case study it investigates strategies for realizing cost efficiencies, ensuring robust knowledge resources, evaluating implementation benefits, and fortifying security measures within RPA environments, offering valuable insights for organizations navigating the complexities of RPA deployment and management.

Pharmacovigilance in Nigeria: Addressing challenges in ensuring drug safety and monitoring adverse effects

This paper examines the specific challenges faced by Nigeria in implementing pharmacovigilance measures to ensure drug safety and monitor adverse effects. Despite the critical importance of pharmacovigilance in safeguarding public health, Nigeria grapples with several obstacles in its implementation. Through a nuanced exploration of literature and regulatory frameworks, this study sheds light on the unique hurdles encountered in pharmacovigilance practices within the Nigerian context. Challenges in Nigeria's pharmacovigilance landscape include limited awareness and understanding of pharmacovigilance principles among healthcare professionals and consumers, resulting in underreporting of adverse drug reactions. Additionally, there is a lack of robust infrastructure and resources for pharmacovigilance activities, including inadequate funding, shortage of trained personnel, and deficient pharmacovigilance databases. Regulatory enforcement and oversight are also hindered by issues such as weak regulatory frameworks, fragmented reporting systems, and suboptimal collaboration among regulatory agencies. Furthermore, Nigeria's pharmacovigilance efforts are compounded by factors such as the proliferation of counterfeit and substandard medicines, informal drug markets, and cultural beliefs influencing healthcare-seeking behaviors. These challenges pose significant barriers to effective pharmacovigilance implementation and compromise patient safety and public health outcomes in Nigeria. Addressing these challenges requires a multifaceted approach encompassing regulatory reforms, capacity building initiatives, public awareness campaigns, and collaboration with international partners. Strengthening regulatory frameworks, enhancing healthcare professional training on pharmacovigilance, establishing robust reporting systems, and fostering partnerships between regulatory agencies, healthcare institutions, and pharmaceutical companies are essential steps toward improving pharmacovigilance practices in Nigeria. Through a thorough examination of the Nigerian context, this paper aims to raise awareness of the specific challenges hindering pharmacovigilance implementation in the country and stimulate dialogue on potential solutions to enhance drug safety and monitoring of adverse effects in Nigeria's healthcare system.

Revolutionizing machine learning: Harnessing hardware accelerators for enhanced AI efficiency

In recent decades, the field of Artificial Intelligence (AI) has undergone a remarkable evolution, with machine learning emerging as a pivotal subdomain. This transformation has led to increasingly complex algorithms and soaring data volumes, necessitating robust computational resources. Conventional central processing units (CPUs) are struggling to meet the demanding requirements of modern AI applications. In response to this computational challenge, a new generation of hardware accelerators has been developed to enhance the processing and learning capabilities of machine learning systems. Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and Application Specific Integrated Circuits (ASICs) are among the specialized accelerators that have emerged. These hardware accelerators have proven instrumental in significantly improving the efficiency of machine learning tasks. This paper provides a comprehensive exploration of these hardware accelerators, offering insights into their design, functionality, and applications. Moreover, it examines their role in empowering machine learning processes and discusses their potential impact on the future of AI. By addressing current trends and anticipated challenges, this paper contributes to a deeper understanding of the dynamic landscape of hardware acceleration in the context of machine learning research and development.

A fuzzy synthetic evaluation approach to assess the risks associated with municipal waste management: Implications for sustainability

The rapid growth of industry, urban areas, and populations has substantially increased waste production worldwide. The inadequacy of robust waste management systems and limited resources poses significant challenges for many emerging economies, including Bangladesh, resulting in environmental contamination. Despite numerous academic studies on sustainable waste management, there is a noticeable gap in the literature concerning identifying, evaluating, and modeling critical risks associated with adopting sustainable municipal waste management systems, particularly in emerging economies like Bangladesh. This study, therefore, aims to fill this gap by identifying, categorizing, and assessing the crucial risks in implementing sustainable municipal waste management systems. Through an extensive review of existing literature and validation with expert feedback, this study employs a fuzzy synthetic evaluation method-based framework to prioritize the identified risks. The study calculates the probability of occurrence and degree of impact values to provide decision-makers with insights into the severity of different risk categories, aiding in case-by-case attention. Scores assigned on a 1 to 5 scale range from 2.59 to 4.14 for the various risk categories. Notably, “Industrial challenges related” and “Community and infrastructure requirements related” emerge as the most significant risk categories among those analyzed. This study contributes to the existing literature by employing fuzzy synthetic evaluation, a methodology rarely used to determine critical risks in municipal waste management. Furthermore, it is poised to offer valuable insights to industrialists and waste management authorities, empowering them to develop responsive strategies and contribute to the sustainable development of cities and urban communities.

Radicalizing safety: A critical narrative analysis to abolish the police

AbstractThe dominant narrative in much of the world is that public safety is provided by policing, evidenced by supportive rhetoric from institutional forces including politicians, media, and large budget allocations in all levels of government. Alongside a long history of police violence, especially against Black, Brown, poor, and other marginalized people, many social movements reject the idea that policing provides safety and seek other methods for community wellness. The present study utilizes critical narrative analysis (CNA) to describe how marginalized residents of a small city in Iowa construct their understanding of personal and community safety. Their stories and the dialectic exchange during interviews illustrated several counternarratives and moments of conscientization for participants and researchers where safety was deconstructed and understood outside the power of recycled institutional narratives. Participants rejected popular notions of safety such as police, and instead embraced safety through robust relationships, community resources, and forms of self‐knowledge such as mental health. We analyzed their interviews as efforts to be humanly recognized within violent white supremacist structures, and their stories help to radicalize popular messages about safety. We highlight their world‐making abilities as they craft their own networks of community and safety outside of the state and police.