Pilot Evaluation of Model Based Design Tooling for Guidance, Navigation, and Control Flight Software Development

No abstract availabl

Elastic Model Transitions: A Hybrid Approach Utilizing Quadratic Inequality Constrained Least Squares (LSQI) and Direct Shape Mapping (DSM)

A method for transitioning linear time invariant (LTI) models in time varying simulation is proposed that utilizes a hybrid approach for determining physical displacements by augmenting the original quadratically constrained least squares (LSQI) algorithm with Direct Shape Mapping (DSM) and modifying the energy constraints. The approach presented is applicable to simulation of the elastic behavior of launch vehicles and other structures that utilize discrete LTI finite element model (FEM) derived mode sets (eigenvalues and eigenvectors) that are propagated throughout time. The time invariant nature of the elastic data presents a problem of how to properly transition elastic states from the prior to the new model while preserving motion across the transition and ensuring there is no truncation or excitation of the system. A previous approach utilizes a LSQI algorithm with an energy constraint to effect smooth transitions between eigenvector sets with no requirement that the models be of similar dimension or have any correlation. This approach assumes energy is conserved across the transition, which results in significant non-physical transients due to changing quasi-steady state energy between mode sets, a phenomenon seen when utilizing a truncated mode set. The computational burden of simulating a full mode set is significant so a subset of modes is often selected to reduce run time. As a result of this truncation, energy between mode sets may not be constant and solutions across transitions could produce non-physical transients. In an effort to abate these transients an improved methodology was developed based on the aforementioned approach, but this new approach can handle significant changes in energy across mode set transitions. It is proposed that physical velocities due to elastic behavior be solved for using the LSQI algorithm, but solve for displacements using a two-step process that independently addresses the quasi-steady-state and non-steady-state contributions to the elastic displacement. For structures subject to large external forces, such as thrust or atmospheric drag, it is imperative to capture these forces when solving for elastic displacement. To simplify the mathematical formulation, assumptions are made regarding mass matrix normalization, constant external forcing, and constant viscous damping. These simplifications allow for direct solutions to the quasi-steady-state displacements through a process titled Direct Shape Mapping. DSM solves for the displacements using the eigenvalues of the elastic modes and the external forcing and returns a set of elastic displacements dictated by the eigenvectors of the post-transition mode set. For the non-steady-state contributions to displacement we formulate a LSQI problem that is constrained by energy of the non-steady state terms. The contributions from the quasi-steady-state and non-steady state solutions are then combined to obtain the physical displacements associated with the new set of eigenvectors. Results for the LSQI-DSM approach show significant reduction/complete removal of transients across mode set transitions while maintaining elastic motion from the prior state. For time propagation applications employing discrete elastic models that need to be transitioned in time and where running with full a full mode set is not feasible, the method developed offers a practical solution to simulating vehicle elasticity

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

The role of analogy and metaphor in the framing and legitimization of strategic change

Strategic change initiatives disrupt established categories of stakeholder understanding and typically present a problem of justifying and legitimizing the change to stakeholders in order to gain their buy-in and support. While it has been suggested that the analogical or metaphorical framing of strategic changes is crucial in that it fosters understanding and creates legitimacy for the change, we set out to specify the conditions and uses of analogical and metaphorical framing in effecting support for strategic changes. Specifically, we argue that (a) analogies are more effective in the context of additive changes, whereas metaphors are more apt for substitutive changes, and that (b) relational analogies and metaphors are generally more effective in securing support for strategic changes, as opposed to analogies or metaphors that highlight common attributes. We also argue that the overall effectiveness of analogies and metaphors in the framing of a change is furthermore dependent on (c) the degree to which these frames are culturally familiar to stakeholders and (d) the extent to which they connect with the prior motivations of stakeholders. © The Author(s) 2011

Resources access needs and capabilities as mediators of the relationship between VC firm size and syndication

Drawing from the resource-based view and transaction costs economics, we develop a theoretical framework to explain why small and large firms face different levels of resource access needs and resource access capabilities, which mediate the relationship between firm size and hybrid governance. Employing a sample of 317 venture capital firms, drawn across six European countries, we empirically assess our framework in the context of venture capital syndication. We estimate a path model using structural equation modeling and find, consistent with our theoretical framework, mediating effects of different types of resource access needs and resource access capabilities between VC firm size and syndication frequency. These findings advance the small business literature by highlighting the trade-offs that size imposes on firms that seek to manage their access to external resources through hybrid governance strategies