SYSTEMS ENGINEERING AND ASSURANCE MODELING (SEAM): A WEB-BASED SOLUTION FOR INTEGRATED MISSION ASSURANCE

We present an overview of the Systems Engineering and Assurance Modeling (SEAM) platform, a web-browser-based tool which is designed to help engineers evaluate the radiation vulnerabilities and develop an assurance approach for electronic parts in space systems. The SEAM framework consists of three interconnected modeling tools, a SysML compatible system description tool, a Goal Structuring Notation (GSN) visual argument tool, and Bayesian Net and Fault Tree extraction and export tools. The SysML and GSN sections also have a coverage check application that ensures that every radiation fault identified on the SysML side is also addressed in the assurance case in GSN. The SEAM platform works on space systems of any degree of radiation hardness but is especially helpful for assessing radiation performance in systems with commercial-off-the-shelf (COTS) electronic components

Connecting Mission Profiles and Radiation Vulnerability Assessment

Radiation vulnerability assessment early in spacecraft development is cheaper and faster than in late development phases. RGENTIC and SEAM are two software platforms that can be coupled to provide this type of early assessment. Specifically, RGENTIC is a tool that outputs descriptions of radiation risks based on a selected mission environment and the system’s electronic part portfolio, while SEAM models how radiation-induced faults in electronic parts propagate through a system. In this work, we propose a spacecraft evaluation flow where RGENTIC’s outputs, which are radiation vulnerabilities of electronic parts for a given mission, become inputs to SEAM, resulting in an automatic part-type template palette presented to users so that they can easily begin modeling the occurrence and propagation of radiation-induced faults in their spacecraft. In this context, fault propagation modeling shows how radiation effects impact the spacecraft’s electronics. The interface between these platforms can be streamlined through the creation of a SEAM global part-type library with templates based on radiation effects in part-type families such as sensors, processors, voltage regulators, and so forth. Several of the part-types defined in RGENTIC have been integrated into SEAM templates. Ultimately, all 66+ part-types from the RGENTIC look-up table will be included in the SEAM global part library. Once accomplished, the part templates can be used to populate each project-specific part library in SEAM, ensuring all RGENTIC’s part-types are represented, and the radiation effects are consistent between the two. The harmonization process between RGENTIC and SEAM begins as follows: designers input a detailed knowledge of their system and mission into RGENTIC, which then outputs a generic part-type list that associates each part-type with potential radiation concerns. The list is then downloaded in a SEAM-readable file, which SEAM uses to populate the initially blank project with the part templates that correspond to RGENTIC’s output. The final product is a system fault model using a project-specific radiation effect part library. The radiation effects considered in the part library are associated with three categories of radiation-environment issues: single event effects (SEE), total ionizing dose (TID), and displacement damage dose (DDD). An example part-type is the discrete LED, which has been functionally decomposed into input power and output light. It has a single possible radiation-induced fault that is associated with DDD, which causes degraded brightness and is observed on the output. Overall, designers will benefit from a coordination of these two tools because it simplifies the initial definition of the project in SEAM. This is especially the case for new users, since the necessary radiation models for their parts are available before modeling commences. Furthermore, starting from a duplicate of an existing project decreases the amount of time and effort required to develop project-specific models. Incorporating RGENTIC’s table of part-types resolves these issues and provides a streamlined process for creating system radiation fault models. Consequently, spacecraft designers can identify radiation problems early in the design cycle and fix them with lower cost and less effort than in later design stages

The RadFxSat-2 Mission to Measure SEU Rates in FinFET Microelectronics

The RadFxSat-2 mission was launched January 17, 2021 with Virgin Orbit\u27s LauncherOne under the NASA ELaNa-20 initiative. RadFxSat-2 carries a radiation effects payload designed to investigate single event upsets (SEUs) in sub-65 nm commercial memories, including a FinFET-based memory. Sub-65 nm technologies have demonstrated enhanced sensitivity to low-energy protons, but current models have not considered low-energy protons as a source of SEUs. Missions utilizing the latest commercial technologies could experience a higher error rate than predicted. RadFxSat-2 was designed to assess SEU rates for FinFET SRAMs operated in low-Earth orbit (LEO), a proton-heavy environment. Details of the mission and data collected over the previous two years are presented. Results from RadFxSat-2 suggest that FinFET-based microelectronic technologies are suitable for high-performance, high-density storage in LEO

Methodology for Correlating Historical Degradation Data to Radiation-Induced Degradation System Effects in Small Satellites

When constructing a system-level fault tree to demonstrate device-to-system level radiation degradation, reliability engineers need relevant, device-level failure probabilities to incorporate into reliability models. Deriving probabilities from testing can be expensive and time-consuming, especially if the system is complex. This methodology offers an alternative means of deriving device-level failure probabilities. It uses Bayesian analysis to establish links between historical radiation datasets and failure probabilities. A demonstration system for this methodology is provided, which is a TID response of a linear voltage regulator at 100 krad(SiO2). Data fed into the Bayesian model is derived from literature on the components found within a linear voltage regulator. An example is presented with data pertaining to the device’s bipolar junction transistor (BJT)’s gain degradation factor (GDF). Kernel density estimation is used to provide insight into the dataset’s general distribution shape. This guides the engineer into picking the appropriate distribution for device-level Bayesian analysis. Failure probabilities generated from the Bayesian analysis are incorporated into a LTspice model to derive a system failure probability (using Monte Carlo) of the regulator’s output. In our demonstration system, a 96.5% likelihood of system degradation was found in the assumed environment

The influence of perceived stress on the human microbiome

Objective Microbial dysbiosis, a shift from commensal to pathogenic microbiota, is often associated with mental health and the gut&ndash;brain axis, where dysbiosis in the gut may be linked to dysfunction in the brain. Many studies focus on dysbiosis induced by clinical events or traumatic incidents; however, many professions in austere or demanding environments may encounter continuously compounded stressors. This study seeks to explore the relationship between microbial populations and stress, both perceived and biochemical. Results Eight individuals enrolled in the study to provide a longitudinal assessment of the impact of stress on gut health, with four individuals providing enough samples for analysis. Eleven core microbial genera were identified, although the relative abundance of these genera and other members of the microbial population shifted over time. Although our results indicate a potential relationship between perceived stress and microbial composition of the gut, no association with biochemical stress was observed. Increases in perceived stress seem to elucidate a change in potentially beneficial Bacteroides, with a loss in Firmicutes phyla. This shift occurred in multiple individuals, whereas using cortisol as a stress biomarker showed contradictory responses. These preliminary data provide a potential mechanism for gut monitoring, while identifying targets for downstream modulation. &nbsp;</p

Stress and worry in the 2020 coronavirus pandemic : relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey

The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that individuals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at https://osf.io/g2t3b. This preregistration was performed prior to data analysis.Peer reviewe

COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak

This N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey - an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.Measurement(s) psychological measurement center dot anxiety-related behavior trait center dot Stress center dot response to center dot Isolation center dot loneliness measurement center dot Emotional Distress Technology Type(s) Survey Factor Type(s) geographic location center dot language center dot age of participant center dot responses to the Coronavirus pandemic Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location global Machine-accessible metadata file describing the reported data:Peer reviewe

Stress and worry in the 2020 coronavirus pandemic: Relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey

The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that individuals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at https://osf.io/g2t3b. This preregistration was performed prior to data analysis

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707