State Differences in the Application of Medical Frailty under the Affordable Care Act

This poster explains a study that examines how states undergoing Medicaid expansion differ in their treatment of the “medically frail” population. The medically frail are individuals who may need the extra benefits offered by traditional Medicaid. The results provide needed information to policymakers that are interested in improving access among vulnerable populations in the 23 states that have not yet implemented Medicaid expansion, but may do so in the future. While regulations provide categories that qualify for medical frailty, each state is free to use their own method of determining who meets the definition. There is a need for ongoing study to determine whether state differences in how medical frailty is addressed are associated with differences in access by persons with high medical need. Presented at the AcademyHealth Annual Research Meeting

Polar curved polycyclic aromatic hydrocarbons in soot formation

© 2018 The Combustion Institute. In this paper, we consider the impact of polar curved polycyclic aromatic hydrocarbons (cPAH) on the process of soot formation by employing electronic structure calculations to determine the earliest onset of curvature integration and the binding energy of curved homodimers. The earliest (smallest size) onset of curvature integration was found to be a six ring PAH with at least one pentagonal ring. The σ bonding in the presence of pentagons led to curvature, however, the π bonding strongly favored a planar geometry delaying the onset of curvature and therefore the induction of a flexoelectric dipole moment. The binding energies of cPAH dimers were found to be of similar magnitude to flat PAH containing one or two pentagons, with an alignment of the dipole moments vectors. For the more curved structures, steric effects reduced the dispersion interactions to significantly reduce the interaction energy compared with flat PAH. Homogeneous nucleation of cPAH at flame temperatures then appears unlikely, however, significant interactions are expected between chemi-ions and polar cPAH molecules suggesting heterogeneous nucleation should be explored.This project is supported by the National Research Foundation (NRF), Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme

Universal Digital Twin - A Dynamic Knowledge Graph

AbstractThis paper introduces a dynamic knowledge-graph approach for digital twins and illustrates how this approach is by design naturally suited to realizing the vision of a Universal Digital Twin. The dynamic knowledge graph is implemented using technologies from the Semantic Web. It is composed of concepts and instances that are defined using ontologies, and of computational agents that operate on both the concepts and instances to update the dynamic knowledge graph. By construction, it is distributed, supports cross-domain interoperability, and ensures that data are connected, portable, discoverable, and queryable via a uniform interface. The knowledge graph includes the notions of a “base world” that describes the real world and that is maintained by agents that incorporate real-time data, and of “parallel worlds” that support the intelligent exploration of alternative designs without affecting the base world. Use cases are presented that demonstrate the ability of the dynamic knowledge graph to host geospatial and chemical data, control chemistry experiments, perform cross-domain simulations, and perform scenario analysis. The questions of how to make intelligent suggestions for alternative scenarios and how to ensure alignment between the scenarios considered by the knowledge graph and the goals of society are considered. Work to extend the dynamic knowledge graph to develop a digital twin of the UK to support the decarbonization of the energy system is discussed. Important directions for future research are highlighted.This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. Part of the research was also funded by the European Commission, Horizon 2020 Programme, DOME 4.0 Project, GA 953163. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Additional support for a number of PhD studentships was provided by Computational Modelling Cambridge Ltd. M.K. gratefully acknowledges the support of the Alexander von Humboldt Foundation

Model Guided Application for Investigating Particle Number (PN) Emissions in GDI Spark Ignition Engines

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Model guided application (MGA) combining physico-chemical internal combustion engine simulation with advanced analytics offers a robust framework to develop and test particle number (PN) emissions reduction strategies. The digital engineering workflow presented in this paper integrates the &lt;i&gt;k&lt;/i&gt;inetics &amp;amp; SRM Engine Suite with parameter estimation techniques applicable to the simulation of particle formation and dynamics in gasoline direct injection (GDI) spark ignition (SI) engines. The evolution of the particle population characteristics at engine-out and through the sampling system is investigated. The particle population balance model is extended beyond soot to include sulphates and soluble organic fractions (SOF). This particle model is coupled with the gas phase chemistry precursors and is solved using a sectional method. The combustion chamber is divided into a wall zone and a bulk zone and the fuel impingement on the cylinder wall is simulated. The wall zone is responsible for resolving the distribution of equivalence ratios near the wall, a factor that is essential to account for the formation of soot in GDI SI engines. In this work, a stochastic reactor model (SRM) is calibrated to a single-cylinder test engine operated at 12 steady state load-speed operating points. First, the flame propagation model is calibrated using the experimental in-cylinder pressure profiles. Then, the population balance model parameters are calibrated based on the experimental data for particle size distributions from the same operating conditions. Good agreement was obtained for the in-cylinder pressure profiles and gas phase emissions such as NO&lt;sub&gt;x&lt;/sub&gt;. The MGA also employs a reactor network approach to align with the particle sampling measurements procedure, and the influence of dilution ratios and temperature on the PN measurement is investigated. Lastly, the MGA and the measurements procedure are applied to size-resolved chemical characterisation of the emitted particles.&lt;/div&gt;&lt;/div&gt;</jats:p

Adversarial Initialization - when your network performs the way I want -

The increase in computational power and available data has fueled a wide deployment of deep learning in production environments. Despite their successes, deep architectures are still poorly understood and costly to train. We demonstrate in this paper how a simple recipe enables a market player to harm or delay the development of a competing product. Such a threat model is novel and has not been considered so far. We derive the corresponding attacks and show their efficacy both formally and empirically. These attacks only require access to the initial, untrained weights of a network. No knowledge of the problem domain and the data used by the victim is needed. On the initial weights, a mere permutation is sufficient to limit the achieved accuracy to for example 50% on the MNIST dataset or double the needed training time. While we can show straightforward ways to mitigate the attacks, the respective steps are not part of the standard procedure taken by developers so far

Evaluating Emissions in a Modern Compression Ignition Engine Using Multi-Dimensional PDF-Based Stochastic Simulations and Statistical Surrogate Generation

© 2018 SAE International. All Rights Reserved. Digital engineering workflows, involving physico-chemical simulation and advanced statistical algorithms, offer a robust and cost-effective methodology for model-based internal combustion engine development. In this paper, a modern Tier 4 capable Cat® C4.4 engine is modelled using a digital workflow that combines the probability density function (PDF)-based Stochastic Reactor Model (SRM) Engine Suite with the statistical Model Development Suite (MoDS). In particular, an advanced multi-zonal approach is developed and applied to simulate fuels, in-cylinder combustion and gas phase as well as particulate emissions characteristics, validated against measurements and benchmarked with respect to the predictive power and computational costs of the baseline model. The multi-zonal SRM characterises the combustion chamber on the basis of different multi-dimensional PDFs dependent upon the bulk or the thermal boundary layer in contact with the cylinder liner. In the boundary layer, turbulent mixing is significantly weaker and heat transfer to the liner alters the combustion process. The integrated digital workflow is applied to perform parameter estimation based on the in-cylinder pressure profiles and engine-out emissions (i.e. NOx, CO, soot and unburnt hydrocarbons; uHCs) measurements. Four DoE (design-of-experiments) datasets are considered, each comprising measurements at a single load-speed point with various other operating conditions, which are then used to assess the capability of the calibrated models in mimicking the impact of the input variable space on the combustion characteristics and emissions. Both model approaches predict in-cylinder pressure profiles, NOx, and soot emissions satisfactorily well across all four datasets. Capturing the physics of emission formation near the cylinder liner enables the multi-zonal SRM approach to provide improved predictions for intermediates, such as CO and uHCs, particularly at low load operating points. Finally, fast-response surrogates are generated using the High Dimensional Model Representation (HDMR) approach, and the associated global sensitivities of combustion metrics and emissions are also investigated