Predicting Responses to Mechanical Ventilation for Preterm Infants with Acute Respiratory Illness using Artificial Neural Networks

Infants born prematurely are particularly susceptible to respiratory illness due to underdeveloped lungs, which can often result in fatality. Preterm infants in acute stages of respiratory illness typically require mechanical ventilation assistance, and the efficacy of the type of mechanical ventilation and its delivery has been the subject of a number clinical studies. With recent advances in machine learning approaches, particularly deep learning, it may be possible to estimate future responses to mechanical ventilation in real‐time, based on ventilation monitoring up to the point of analysis. In this work, recurrent neural networks are proposed for predicting future ventilation parameters due to the highly nonlinear behavior of the ventilation measures of interest and the ability of recurrent neural networks to model complex nonlinear functions. The resulting application of this particular class of neural networks shows promise in its ability to predict future responses for different ventilation modes. Towards improving care and treatment of preterm newborns, further development of this prediction process for ventilation could potentially aid in important clinical decisions or studies to improve preterm infant health

A generalized computationally efficient inverse characterization approach combining direct inversion solution initialization with gradient-based optimization

A computationally efficient gradient-based optimization approach for inverse material characterization from incomplete system response measurements that can utilize a generally applicable parameterization (e.g., finite element-type parameterization) is presented and evaluated. The key to this inverse characterization algorithm is the use of a direct inversion strategy with Gappy proper orthogonal decomposition (POD) response field estimation to initialize the inverse solution estimate prior to gradient-based optimization. Gappy POD is used to estimate the complete (i.e., all components over the entire spatial domain) system response field from incomplete (e.g., partial spatial distribution) measurements obtained from some type of system testing along with some amount of a priori information regarding the potential distribution of the unknown material property. The estimated complete system response is used within a physics-based direct inversion procedure with a finite element-type parameterization to estimate the spatial distribution of the desired unknown material property with minimal computational expense. Then, this estimated spatial distribution of the unknown material property is used to initialize a gradient-based optimization approach, which uses the adjoint method for computationally efficient gradient calculations, to produce the final estimate of the material property distribution. The three-step [(1) Gappy POD, (2) direct inversion, and (3) gradient-based optimization] inverse characterization approach is evaluated through simulated test problems based on the characterization of elastic modulus distributions with localized variations (e.g., inclusions) within simple structures. Overall, this inverse characterization approach is shown to efficiently and consistently provide accurate inverse characterization estimates for material property distributions from incomplete response field measurements. Moreover, the solution procedure is shown to be capable of extrapolating significantly beyond the initial assumptions regarding the potential nature of the unknown material property distribution

Adaptive Reduced-Basis Generation for Reduced-Order Modeling for the Solution of Stochastic Nondestructive Evaluation Problems

A novel algorithm for creating a computationally efficient approximation of a system response that is defined by a boundary value problem is presented. More specifically, the approach presented is focused on substantially reducing the computational expense required to approximate the solution of a stochastic partial differential equation, particularly for the purpose of estimating the solution to an associated nondestructive evaluation problem with significant system uncertainty. In order to achieve this computational efficiency, the approach combines reduced-basis reduced-order modeling with a sparse grid collocation surrogate modeling technique to estimate the response of the system of interest with respect to any designated unknown parameters, provided the distributions are known. The reduced-order modeling component includes a novel algorithm for adaptive generation of a data ensemble based on a nested grid technique, to then create the reduced-order basis. The capabilities and potential applicability of the approach presented are displayed through two simulated case studies regarding inverse characterization of material properties for two different physical systems involving some amount of significant uncertainty. The first case study considered characterization of an unknown localized reduction in stiffness of a structure from simulated frequency response function based nondestructive testing. Then, the second case study considered characterization of an unknown temperature-dependent thermal conductivity of a solid from simulated thermal testing. Overall, the surrogate modeling approach was shown through both simulated examples to provide accurate solution estimates to inverse problems for systems represented by stochastic partial differential equations with a fraction of the typical computational cost

Efficient Global Sensitivity Analysis of Structural Vibration for a Nuclear Reactor System Subject to Nonstationary Loading

The structures associated with the nuclear steam supply system (NSSS) of a pressurized water reactor (PWR) include significant epistemic and aleatory uncertainties in the physical parameters, while also being subject to various non-stationary stochastic loading conditions over the life of a nuclear power plant. To understand the influence of these uncertainties on nuclear reactor systems, sensitivity analysis must be performed. This work evaluates computational design of experiment strategies, which execute a nuclear reactor equipment system finite element model to train and verify Gaussian Process (GP) surrogate models. The surrogate models are then used to perform both global and local sensitivity analyses. The significance of the sensitivity analysis for efficient modeling and simulation of nuclear reactor stochastic dynamics is discussed

Flexural Behaviour of Reinforced Concrete Beams Strengthened with a Composite Reinforcement Layer: BFRP Grid and ECC

In this paper, a new strengthening technique for reinforced concrete (RC) beams is proposed by combining Basalt fiber Reinforced Polymer (BFRP) grid and Engineered Cementitious Composites (ECC) as a composite reinforcement layer (CRL). Five RC beams externally bonded with the CRL at the soffit and one control RC beam were tested to investigate their flexural behaviour. The thickness of BFRP grids (i.e. 1 mm, 3 mm and 5 mm) and the bonded length of CRL (i.e. 400 mm, 450 mm and 500 mm) were selected as two main parameters in the test program, while the width and thickness of CRL were fixed approximately at 200 mm and 30 mm, respectively. The test results showed that there is no clear CRL debonding in the strengthened beams. The two final failure modes were concrete crushing or rupture of the BFRP grids, indicating that the proposed technique is effective in suppressing the debonding of externally bonded materials and fully utilizing the material strengths. An analytical model is also presented to predict the load-deflection responses of the strengthened beams, which was validated through comparisons with the test results

Computational Design Optimization of a Smart Material Shape Changing Building Skin Tile

The development and evaluation of a computational approach for optimal design of a smart material shape changing building skin is presented and numerically evaluated. Specifically, a unique shape-based approach is utilized to create an optimization approach to identify the activation and actuation mechanisms to minimize the difference between a desired shape and the estimated morphed shape. Three potential metrics of shape difference are considered and their capability to facilitate an efficient optimization process leading to accurate shape matching is evaluated. Details of the optimal design framework are presented, particularly focusing on the shape difference metrics as well as the strategy to parameterize the activation of the smart material. In particular, the parameterization strategy is a unique approach to easily integrate controllable localized activation within a smart material structure in a generally applicable way that does not limit the design search space. A series of numerical design examples are presented based on the concept of a smart material (e.g., shape memory polymer) shape changing tile that can be activated and actuated in a variety of ways to achieve desirable surface wrinkle patterns. These numerical design examples are applied to both 2D and 3D problems and consider a variety of parameterizations and target shapes. Results indicate that the shape-based approach can consistently determine the mechanisms of morphing needed to accurately match a target shape. Furthermore, it is shown that localized material activation can lead to not only a more accurate shape but also requires less energy and actuation devices to do so

A five-year review of burn injuries in Irrua

<p>Abstract</p> <p>Background</p> <p>The management of burns remains a challenge in developing countries. Few data exist to document the extent of the problem. This study provides data from a suburban setting by documenting the epidemiology of burn injury and ascertaining outcome of management. This will help in planning strategies for prevention of burns and reducing severity of complications.</p> <p>Methods</p> <p>A total of 72 patients admitted for burns between January 1st, 2002 and December 31st, 2006 at the Irrua specialist teaching hospital were studied retrospectively. Sources of information were the case notes and operation registers. Data extracted included demographics as well as treatment methods and outcome</p> <p>Results</p> <p>The results revealed male to female ratio of 2.1:1. Over 50% of the injuries occurred at home. There was a seasonal variation with over 40% of injuries occurring between November and January. The commonest etiologic agent was flame burn from kerosene explosion. There were 7 deaths in the series.</p> <p>Conclusion</p> <p>Burns are preventable. We recommend adequate supply of unadulterated petroleum products and establishment of burn centers.</p