A framework for design assurance in developing embedded systems

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringStephen A. DyerSteven WarrenEmbedded systems control nearly every device we encounter. Examples abound: appliances, scientific instruments, building environmental controls, avionics, communications, smart phones, and transportation subsystems. These embedded systems can fail in various ways: performance, safety, and meeting market needs. Design errors often cause failures in performance or safety. Market failures, particularly delayed schedule release or running over budget, arise from poor processes. Rigorous methods can significantly reduce the probability of failure. Industry has produced and widely published “best practices” that promote rigorous design and development of embedded systems. Unfortunately, 20 to 35% of development teams do not use them, which leads to operational failures or missed schedules and budgets. This dissertation increases the potential for success in designing and developing embedded systems through the following: 1. It identifies, through literature review, the reasons and factors that cause teams to avoid best practices, which in turn contribute to development failures. 2. It provides a framework, as a psychologically unbiased mediator, to help teams institute best practices. The framework is both straightforward to implement and use and simple to learn. 3. It examines the feasibility of both crowdsourcing and the Delphi method to aid, through anonymous comments on proposed projects, unbiased mediation and estimation within the framework. In two separate case studies, both approaches resulted in underestimation of both required time and required effort. The wide variance in the surveys’ results from crowdsourcing indicated that approach to not be particularly useful. On the other hand, convergence of estimates and forecasts in both projects resulted when employing the Delphi method. Both approaches required six or more weeks to obtain final results. 4. It develops a recommendation model, as a plug-in module to the framework, for the build-versus-buy decision in design of subsystems. It takes a description of a project, compares designing a custom unit with integrating a commercial unit into the final product, and generates a recommendation for the build-versus-buy decision. A study of 18 separate case studies examines the sensitivity of 14 parameters in making the build-versus-buy decision when developing embedded systems. Findings are as follows: team expertise and available resources are most important; partitioning tasks and reducing interdependence are next in importance; the quality and support of commercial units are less important; and finally, premiums and product lifecycles have the least effect on the cost of development. A recommendation model incorporates the results of the sensitivity study and successfully runs on 16 separate case studies. It shows the feasibility and features of a tool that can recommend a build-or-buy decision. 5. It develops a first-order estimation model as another plug-in module to the framework. It aids in planning the development of embedded systems. It takes a description of a project and estimates required time, required effort, and challenges associated with the project. It is simple to implement and easy to use; it can be a spreadsheet, a Matlab model or a webpage; each provides an output like the model for the build-versus-buy decision

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease