Critical Uncertainties Research Articles

Evidence of unprecedented rise in growth synchrony from global tree ring records.

Changes in the temporal coherence between populations, which can influence their stability, resilience and persistence, remain a critical uncertainty of climate change. Recent studies have documented increasing spatial synchrony between populations at continental scales and linked it to anthropogenic climate change. However, the lack of long-term and global baseline perspectives on spatial synchrony presents a challenge to understanding the importance of these trends. Here, we show a steady rise in the spatial synchrony of annual tree growth from a global tree ring database over the past 50 years that is consistent across continents, species and environmental conditions and is unprecedented for the past millennium. Increasing growth synchrony coincided with warming trends and potentially rising synchrony in the temperature records. We discuss the potential driving mechanisms and the limitations in the interpretation of this trend, and we propose that increasing mutual dependency on external factors (also known as Moran's effect) linked to rising global temperatures is the most likely driver of more homogeneous global growth dynamics.

The Environmental Impacts of Metal Powder Bed Additive Manufacturing

AbstractAdditive manufacturing (AM) is widely recognized as a critical pillar of advanced manufacturing and is moving from the design shop to the factory floor. As AM processes become more popular, it is paramount that engineers and policymakers understand and then reduce their environmental impacts. This article structures the current work on the environmental impacts of metal powder bed processes: selective laser melting (SLM), direct metal laser sintering (DMLS), electron beam melting (EBM), and binder jetting (BJ). We review the potential benefits and pitfalls of AM in each phase of a part's lifecycle and in different application domains (e.g., remanufacturing and hybrid manufacturing). We highlight critical uncertainties and future research directions throughout. The environmental impacts of AM are sensitive to the specific production and use-phase context; however, several broad lessons can be extracted from the literature. Unlike in conventional manufacturing, powder bed production impacts are dominated by the generation of the direct energy (electricity) required to operate the AM machines. Combined with a more energy-intensive feedstock (metal powder), this means that powder bed production impacts are higher than in conventional manufacturing unless production volumes are very small (saving tool production impacts), and/or there are significant material savings through part light weighting or improved buy-to-fly ratios.

Proposition of a method for stochastic analysis of value streams

This article aims at proposing a method to stochastically analyze values streams taking into consideration the effect of critical uncertainty sources on lead time. The proposed method combines value stream mapping (VSM) and Monte Carlo simulation to identify improvement opportunities. To illustrate this approach, we carried out a case study in the special nutrition value stream of a Brazilian public hospital. Results show that the proposed method allows the identification of improvement opportunities that would not be considered in the classical deterministic VSM approach. Further, the integration of the stochastic analysis enables the determination of a more realistic lead time, which supports a more assertive planning and scheduling of the value stream. The proposed method addresses a fundamental gap in traditional VSM without adding much complexity to the analysis procedure, which is a common practical issue in previous works that integrated other stochastic methods into VSM.

Decision analysis for greater insights into the development and evaluation of Chinook salmon restoration strategies in California's Central Valley

Considerable resources have been invested in ecological restoration projects across the globe to restore ecosystem integrity. Restoration strategies are often diverse and have been met with mixed success. In this article, we describe the Chinook salmon (Oncorhynchus tshawytscha) decision‐support models developed by the Central Valley Project Improvement Act Science Integration Team as part of a larger structured decision‐making effort aimed at maximizing natural adult production of Chinook salmon in California's Central Valley, the United States. We then describe the decision‐analytic tools the stakeholder group used to solve the models and explore model results, including stochastic dynamic programming, forward simulation, proportional scoring, relative loss, expected value of perfect information, response profile analyses, and indifference curves. Using these tools, the stakeholder group was able to develop and evaluate restoration strategies for multiple Chinook salmon runs simultaneously, a first for the restoration program. We found that actions targeted at one run were detrimental to others, which was unexpected. Furthermore, information uncovered during this process was used to direct efforts towards targeted research/monitoring to reduce critical uncertainties in salmon demographic rates and make better restoration decisions moving forward. The decision sciences have established a wide range of analytical tools and approaches to simplify complex problems into key components, and we believe the concepts described in this article are of great interest and can be applied by many restoration practitioners that undoubtedly face similar difficulties when implementing restoration strategies for complex systems.

Measuring the Additive Effects of Predation on Prey Survival Across Spatial Scales

The degree to which predation is an additive vs. compensatory source of mortality is fundamental to understanding the effects of predation on prey populations and evaluating the efficacy of predator management actions. In the Columbia River basin, USA, predation by Caspian Terns (Hydroprogne caspia) on U.S. Endangered Species Act (ESA)-listed juvenile salmonids (smolts; Oncorhynchus spp.) has led to predator management actions to reduce predation; however, the assumption that reduced predation translates into greater salmonid survival, either within the life stage where predation occurs or across their lifetime, has remained untested. To address this critical uncertainty, we analyzed a long-term (2008-2018) mark-recapture-recovery data set of ESA-listed steelhead trout (O. mykiss) that were tagged (n = 78,409) and subsequently exposed to predation during smolt out-migration through multiple river reaches (spatial scales), jointly estimating weekly probabilities of steelhead survival, mortality due to bird predation, and mortality due to other causes. This concurrent estimation across time-stratified cohorts allowed for the direct measurement of the strength, magnitude, and direction of relationships between survival and Caspian Tern predation. Estimates of Tern predation on steelhead were substantial in most years, with cumulative annual estimates ranging from 0.075 (95% creditable interval = 0.058-0.099) to 0.375 (0.290-0.461). Increases in Tern predation probabilities were associated with statistically significant decreases in steelhead survival probabilities in all evaluated years and salmonid life stages (smolt out-migration and smolt-to-adult returns). Results provide novel evidence that predation by Caspian Terns may have been a super-additive source of mortality during the smolt life stage and a partially additive source of mortality to the adult life stage. Annual estimates of the difference between observed survival and baseline survival (i.e., in the absence of Tern predation) ranged from 0.052 (0.017-0.103) to 0.314 (0.172-0.459) during the steelhead smolt life stage and from 0.011 (0.001-0.029) to 0.049 (0.025-0.078) to the adult life stage. The estimated levels of compensation have important implications for predator management actions aimed at increasing the survival of endangered salmonids, and the modeling approach developed herein provides a framework to directly quantify the impacts of source-specific mortality factors on prey populations.

Коронавірус і безпекове середовище: спроба неупередженого аналізу

At the beginning of 2020, humanity was faced with an unprecedented threat – the massive spread of extremely virulent coronavirus disease with the international designation “COVID-19”. The World Health Organization quickly recognized the epidemiological situation as a pandemic. Although a pandemic was contemplated by almost every country, not one of them was ready for it. Almost the only means of counteraction was to reduce the transit movements of people and goods, which immediately led to an economic decline. Therefore, there is a question of anticipating the possible consequences of the impact of a pandemic on changing key conditions of security environment. Such a prediction can be tried to be formed on the basis of a comparative analysis of the current situation with a hypothetical situation, which was analyzed by the Rockefeller Foundation back in 2010. The Fund considered four scenarios obtained by combining two critical uncertainties: the level of political and economic integration and the ability of society for adaptation. As a result of a comparative analysis, it was established that the current situation has the greatest number of similar elements with the scenario of total blocking, which involves strengthening the powers of governments, increasing restrictions on people, controlling them, and increasing attention to security issues. The projection of this scenario on the security environment allows us to conclude that in the future we can expect the US, China, the Russian Federation and the EU to retain their ability to influence international relations, to build their own zones of influence, to increase the powers' attention to economic growth, and also to slightly reduce the number and intensity of military conflicts.

A Digital Twin Approach to a Quantitative Microstructure-Property Study of Carbon Fibers through HRTEM Characterization and Multiscale FEA.

Microstructures of typical carbon fibers (CFs) from polyacrylonitrile (PAN) and pitch-based precursors were studied using a novel digital twin approach with individual carbon fibers for a local crystal scale model. The transmission electron microscopy (TEM) samples were prepared using a focused-ion beam (FIB) for both longitudinal and transverse directions of carbon fibers. Measurements of the crystal size and orientation were estimated from X-ray scattering. TEM imaging of graphitic packing facilitated further comprehension of associations between processing and final material properties, which could enable customization of microstructures for property targets. Then the detailed microstructural information and their X-ray scattering properties were incorporated into the simulation model of an individual carbon fiber. Assuming that graphene properties are the same among different forms of carbon fiber, a reasonable physics-based explanation for such a drastic decrease in strength is the dislocations between the graphitic units. The model reveals critical defects and uncertainty of carbon fiber microstructures, including skin/core alignment differences and propagating fracture before ultimate failure. The models are the first to quantify microstructures at the crystal scale with micromechanics and to estimate tensile and compressive mechanical properties of carbon fiber materials, as well as potentially develop new fundamental understandings for tailoring carbon fiber and composites properties.

A A Scenario-based Modelling Study of the Prevention of Myocardial Infarction in Iran : A Scenario-based and Modeling Study on the Prevention of Heart Attack in Iran

Background and Objectives: Globally, cardiovascular disease (CVD) is the number one cause of mortality. In this regard, this study aimed to provide policies for the management of CVD by focusing on the reduction of myocardial infarction (MI) mortality rate in Iran. Materials and Methods: The sequential mixed methods design will be employed to foresight the prevalence of MI in Iran in the next 10 years. This study consists of five phases and in the first phase, the risk factors of cardiovascular disease will be investigated using a systematic review. In the second phase, the uncertainty and impact of those factors will be demonstrated by the experts. Moreover, the impact/uncertainty grid will be used to identify the drivers that are less important and critical uncertainties. In the third phase, the cross-impact matrix will be developed by Scenario wizard, and the scenario logic and the scenarios will be developed. Once the scenario logic is established, details can be added to the scenarios. The next phase consists of statistical estimations of the rate of mortality due to heart attack using artificial neural networks. Finally, the policies will be developed based on the opinions of the panel of experts. The initial results will be published in mid-2020. Results: This future study will develop policies to prevent from MI with scenario-based and modeling approaches. The findings can be useful for healthcare professionals and it can improve our understanding of the future of MI to enhance the management of MI patients. Conclusion: The obtained policies will help policymakers to make evidence-based decisions, re-design structures, and processes of healthcare interventions, and also plan to decrease MI mortality rate.

Instructional and Business Continuity Amid and Beyond COVID-19 Outbreak: A Case Study from the Higher Colleges of Technology

With the announcement of COVID-19 as pandemic, and the call for social distancing, academic institutions reacted by shutting campuses and calling for the shift to virtual online delivery. In HCT, we conducted this study in an effort to sustain success in these global challenging times of crisis that is informed by strategic foresight.HCT called for an all-online delivery starting March 22, 2020 after a two-day pilot in the preceding week. HCT readiness is a result of orchestrating: an ecosystem perspective on digital transformation, a forth-looking institutional strategy that has technology utilization as a major pillar, an education technology strategy, and a comprehensive set of intelligent learning tools.Forward-looking scenarios were designed based on two critical uncertainties: (1) COVID-19 longevity and (2) socio-economic disruption. These scenarios are: Divine Mercy, Recovery Mode, New Norm, and Survival of the Fittest. Subsequently, the features of each scenario are assessed for implications on HCT’s business and support operations, and the proactive strategies are documented to cope with these implications.During the full online delivery mode period, HCT recorded 86% satisfaction amongst its faculty and 54% amongst its student body, delivered 234,000 hours through 61,000 online classes, and delivered over 21,000 hours of online professional development (PD). Over the same period, more than 1900 non-faculty employees have been running business as usual from home.Envisioning future scenarios and preparing the organization for them is a practice that should be deployed in parallel to emergency response efforts to ensure successful business continuity.

Evidence-Based Medicine in Otolaryngology, Part XI: Modeling and Analysis to Support Decisions.

To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices. Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions. Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations. Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care. Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.

Telebehavioral practice basics for social worker educators and clinicians responding to COVID-19

ABSTRACT Social Work’s Grand Challenge to Harness Technology for Social Good calls for educators to reevaluate their role and its significance for the future of social work. Information and communication technology (ICT)-mediated practice methods like artificial intelligence, virtual reality, gamification, and big data, among others, represent a new arena for social work practice. However, educators have been mostly inactive in developing curricula that support student knowledge, training, and decision-making on the adoption of technology for practice. In the United States, the Council on Social Work Education Futures Task Force highlighted this inaction as a matter of critical uncertainty for the field’s future. In contrast, this paper describes how a school of social work rapidly deployed a free, CE training program on the basics of telebehavioral health practice to the social work community to aid their response to COVID-19. The rapid deployment of that training has been distilled as ‘lessons learned’ for those wanting to join in efforts to address the field’s critical uncertainty regarding the adoption of technology. Information is presented about the reach of this training and includes feedback from participants. Additionally, the authors discuss whether COVID-19 can influence social work’s future rate of technology adoption.

Control power as a special case of protean power: thoughts on Peter Katzenstein and Lucia Seybert'sProtean Power: Exploring the Uncertain and Unexpected in World Politics

AbstractHuman experience of control is an illusion; all forms of power are a special, transient, and unstable case of protean power. Taking risks is governed by critical uncertainty less because of our lack of perfect knowledge than because the world is physically and socially indeterminate. Power, thus, lies not only in agents' potential to dominate each other, but also in acting in concert to turn propensities into reality. Radical uncertainty is, therefore, not necessarily bad news. Whether protean power endangers or protects humanity depends less on calculating risks than on agents practicing common humanity values. I revise Katzenstein's and Seybert's concepts accordingly and illustrate by discussing Artificial Intelligence's challenges to humanity.

An adaptive management approach for implementing multi-jurisdictional response to grass carp in Lake Erie

The Great Lakes are influenced by established aquatic invasive species (AIS) and the threat of new invaders persists. Grass carp, one of four species commonly referred to as Asian carp, are considered invasive because of their ability to adversely modify aquatic habitat through consumption of aquatic macrophytes. Grass carp have been infrequently detected in the Great Lakes since the mid-1980s. More frequent reports of grass carp captures from commercial fishermen in the early 2010’s elevated the concern of the potential risk of colonization in Lake Erie. This paper provides a case study detailing the development and implementation of a multi-jurisdictional response strategy for grass carp in Lake Erie. To respond to threats of grass carp in Lake Erie, Michigan and Ohio Departments of Natural Resources led targeted responses using a collaborative multi-jurisdictional approach, while simultaneously investing in reducing critical life-history uncertainties to refine strategies in an adaptive and science-based manner. Efforts to address uncertainties about grass carp life history documented spawning in two Lake Erie tributaries. Building on these early responses, the binational Lake Erie Committee developed a five-year adaptive response framework to guide response actions. The collaborative response efforts resulted in the capture and removal of 184 fertile grass carp since 2014, and efforts are ongoing to increase effectiveness of strategies to achieve desired population reduction. Coordinated grass carp response actions under the five-year strategy will continue using adaptive management principles with outcomes providing useful insights for adapting existing response frameworks and more broadly for AIS responses implemented elsewhere.

A Bayesian Deep Learning RUL Framework Integrating Epistemic and Aleatoric Uncertainties

Recent years have witnessed the prominent advancements of deep learning (DL) in the arsenal of prognostics and health management. However, the prognostic uncertainty problem extensively existed in industrial devices is not addressed by most DL approaches. This article formulates a novel Bayesian Deep Learning (BDL) framework to characterize the prognostic uncertainties. A distinguished advantage of the framework is its capacity of capturing the comprehensive effects of two critical uncertainties: 1) epistemic uncertainty, accounting for the uncertainty in the model, and 2) aleatoric uncertainty, representing the impact of random disturbance, such as measurement errors. The former arises from the variability of the model weights, and the latter is characterized by selected lifetime distributions. We integrate both uncertainties by defining BDL as priors of lifetime parameters. A sequential Bayesian boosting algorithm is executed to improve the estimation accuracy and compress the credible intervals. The superior prediction performance of our framework is validated by a real-world dataset collected from hydraulic mechanisms of circuit breakers.

Measuring the additive effects of predation on prey survival across spatial scales.

The degree to which predation is an additive vs. compensatory source of mortality is fundamental to understanding the effects of predation on prey populations and evaluating the efficacy of predator management actions. In the Columbia River basin, USA, predation by Caspian Terns (Hydroprogne caspia) on U.S. Endangered Species Act (ESA)-listed juvenile salmonids (smolts; Oncorhynchus spp.) has led to predator management actions to reduce predation; however, the assumption that reduced predation translates into greater salmonid survival, either within the life stage where predation occurs or across their lifetime, has remained untested. To address this critical uncertainty, we analyzed a long-term (2008-2018) mark-recapture-recovery data set of ESA-listed steelhead trout (O. mykiss) that were tagged (n=78,409) and subsequently exposed to predation during smolt out-migration through multiple river reaches (spatial scales), jointly estimating weekly probabilities of steelhead survival, mortality due to bird predation, and mortality due to other causes. This concurrent estimation across time-stratified cohorts allowed for the direct measurement of the strength, magnitude, and direction of relationships between survival and Caspian Tern predation. Estimates of Tern predation on steelhead were substantial in most years, with cumulative annual estimates ranging from 0.075 (95% creditable interval=0.058-0.099) to 0.375 (0.290-0.461). Increases in Tern predation probabilities were associated with statistically significant decreases in steelhead survival probabilities in all evaluated years and salmonid life stages (smolt out-migration and smolt-to-adult returns). Results provide novel evidence that predation by Caspian Terns may have been a super-additive source of mortality during the smolt life stage and a partially additive source of mortality to the adult life stage. Annual estimates of the difference between observed survival and baseline survival (i.e., in the absence of Tern predation) ranged from 0.052 (0.017-0.103) to 0.314 (0.172-0.459) during the steelhead smolt life stage and from 0.011 (0.001-0.029) to 0.049 (0.025-0.078) to the adult life stage. The estimated levels of compensation have important implications for predator management actions aimed at increasing the survival of endangered salmonids, and the modeling approach developed herein provides a framework to directly quantify the impacts of source-specific mortality factors on prey populations.

Model-based evaluation of management options in ornamental plant nurseries

Agricultural management decisions are usually made without perfect knowledge. Decision Analysis (DA) approaches translate available uncertain information on costs, benefits and risks involved in decisions into actionable management recommendations. We illustrate the use of DA procedures to inform decisions on disease management strategies in ornamental plant production. We worked with heather growers and other stakeholders in North Rhine-Westphalia, Germany, to model the impacts of changing disease management practices and to generate comprehensive forecasts of net returns. Through sensitivity analysis and Value of Information assessment we identified critical uncertainties regarding the feasibility of improved practices. Partial Farm Budgets for decision options ranged from a loss of more than 200,000 € to a gain of nearly 70,000 € per hectare and year. Findings suggest that reducing pesticide applications without additional monitoring may substantially increase production risks (chance of loss of 76%) and that intensified plant monitoring is likely to increase net benefits (chance of gain of 68%) by allowing earlier detection and more focused fungicide application. Our Decision Analysis approach facilitated ex-ante evaluation of innovative management strategies in heather production, and it holds promise for similar evaluations in other agricultural production systems.

HIV and antiretroviral therapy-related fat alterations.

Early in the HIV epidemic, lipodystrophy, characterized by subcutaneous fat loss (lipoatrophy), with or without central fat accumulation (lipohypertrophy), was recognized as a frequent condition among people living with HIV (PLWH) receiving combination antiretroviral therapy. The subsequent identification of thymidine analogue nucleoside reverse transcriptase inhibitors as the cause of lipoatrophy led to the development of newer antiretroviral agents; however, studies have demonstrated continued abnormalities in fat and/or lipid storage in PLWH treated with newer drugs (including integrase inhibitor-based regimens), with fat gain due to restoration to health in antiretroviral therapy-naive PLWH, which is compounded by the rising rates of obesity. The mechanisms of fat alterations in PLWH are complex, multifactorial and not fully understood, although they are known to result in part from the direct effects of HIV proteins and antiretroviral agents on adipocyte health, genetic factors, increased microbial translocation, changes in the adaptive immune milieu after infection, increased tissue inflammation and accelerated fibrosis. Management includes classical lifestyle alterations with a role for pharmacological therapies and surgery in some patients. Continued fat alterations in PLWH will have an important effect on lifespan, healthspan and quality of life as patients age worldwide, highlighting the need to investigate the critical uncertainties regarding pathophysiology, risk factors and management.

Value of Information Analysis for Complex Simulator Models: Application to Wind Farm Maintenance

Many domains require the use of sophisticated simulators to adequately model the effect of chosen alternatives on the decision maker’s value. Decision support in such complex systems brings unique challenges around efficiency, because simulating each combination of inputs can be time-consuming. In this paper, we conduct a value of information (VOI) analysis to study whether one should purchase information about critical input uncertainties in such complex systems. We propose a novel computational approach, where Gaussian processes are used to model the decision maker’s profit as a function of different alternatives and uncertainties. Under this modeling assumption, the expected improvement of the profit is analytically available, which we use to approximate the VOI effectively over batches of simulations, thus avoiding too many computer-intensive evaluations of the system. We illustrate the proposed computational approach with an offshore wind farm maintenance application, where the decision maker relies on outputs from large-scale simulations to determine the optimal vessel fleet mix and number of personnel for operation and maintenance. Such computer-intensive simulations mimic long-term energy production under different input conditions. It is often not possible to explore all the alternatives exhaustively, and one must therefore guide the simulations to run on promising alternatives. We conduct a VOI analysis to study whether one should purchase information about failure rates of wind turbines for this application. The proposed methodologies are general and apply to other domains involving expensive simulators.

Identifying research needs to inform white‐nose syndrome management decisions

AbstractEcological understanding of host–pathogen dynamics is the basis for managing wildlife diseases. Since 2008, federal, state, and provincial agencies and tribal and private organizations have collaborated on bat and white‐nose syndrome (WNS) surveillance and monitoring, research, and management programs. Accordingly, scientists and managers have learned a lot about the hosts, pathogen, and dynamics of WNS. However, effective mitigation measures to combat WNS remain elusive. Host–pathogen systems are complex, and identifying ecological research priorities to improve management, choosing among various actions, and deciding when to implement those actions can be challenging. Through a cross‐disciplinary approach, a group of diverse subject matter experts created an influence diagram used to identify uncertainties and prioritize research needs for WNS management. Critical knowledge gaps were identified, particularly with respect to how WNS dynamics and impacts may differ among bat species. We highlight critical uncertainties and identify targets for WNS research. This tool can be used to maximize the likelihood of achieving bat conservation goals within the context and limitations of specific real‐world scenarios.

An Accessible Method for Screening Aerosol Filtration Identifies Poor-Performing Commercial Masks and Respirators

Background: The COVID-19 pandemic has presented an acute need for masks, specifically N95 respirators to be used by healthcare workers contacting COVID-19 patients and medical masks to be used by the public. The shortage of regulation NIOSH-tested masks make it necessary to find alternatives to protect frontline healthcare workers. Many of the available alternatives have not been NIOSH-certified or tested in the United States, leaving room for question as to whether they are performing at a level that is protecting the wearer from SARS-CoV-2 transmission. In addition, currently there are many counterfeit N95 and other masks being produced and sold as authentic products. It has become critical to be able to test these masks from non-traditional suppliers as well as the many masks that are being donated to hospitals at this time in order to ensure the safety of healthcare workers. Methods: For situations where regulatory methods are not accessible, we present an experimental setup and screening method to evaluate mask filtration and breathability quickly via a cost-effective approach that could be replicated in communities of need without the extensive research infrastructure necessary for regulation mask tests. The experimental approaches presented here examined both mask aerosol filtration performance and mask flow impedance to ensure breathability, and mask fit tests were conducted in tandem but are not the focus of this study. Findings: Tests conducted of 28 non-regulation masks using this setup reveal that a number of commercially- available masks in hospital inventories perform similarly to N95 masks for aerosol filtration of 0.2 μm and above. There are also a range of masks with relatively-weaker filtration efficiencies. Yet, a subset of commercially-available masks have poor filtration performance relative to N95 or similar masks at typical breathing velocities. All masks functioned acceptably with regards to breathability (i.e. flow impedance) and impedance was not correlated with filtration efficiency. Interpretation: With a small set of simplified tests, organizations with critical personal protective equipment (PPE) shortages and uncertainties about their mask/respirator inventories can quickly evaluate the efficacy of their masks relative to N95-regulation masks to make informed decisions about which PPE to use and future procurement. This is essential since the results of this study use an actual diverse inventory of masks/respirators from a hospital network and show significant variability in their performance for filtering airborne aerosols that could contain SARS-CoV-2 and a subset of masks that would not be acceptable for frontline workers.