Care of the transgender population: are the Indian gynaecology residents sufficiently trained?

Background: Over the past few years awareness on gender dysphoria has risen but accessing healthcare as a transgender person (TGP) is still challenging. Deficits in training of residents may contribute to disparities impacting their health. Hence, the pressing priority is to understand the unique need of the TGP and be equipped to offer them competent care. The aim of the current study was to assess the comfort, knowledge and training of the Obstetrics and Gynaecology residents in Tamil Nadu with regard to transgender healthcare. Methods: This was a cross sectional survey sent as a Google Form electronically to 100 residents who had recently completed their residency in obstetrics and gynecology in Tamil Nadu. The questions were designed to assess their knowledge and experience in the care of transgender population. Microsoft Excel software was used to analyze the results Results: The response rate for the survey was 67%. Among them, 47.1% of the residents were unaware of the current recommendations for Gender Reassignment Surgery. The expertise related to transition of people with gender dysphoria and the hormonal regimen wanted for them were lacking. While half of the residents realized their lack of competency in caring for TGP, 98.5% were ready for under going further training to improve their knowledge. Conclusions: It was observed that the residents did not have enough training and competency to adequately care for the TGP though they are ready to upskill their knowledge. Hence, efforts should be made to incorporate training modules for TGP care in Indian OBG residency curriculum and train the upcoming residents to offer quality care to all

Parameter Estimation for Physics-Based Electrochemical Model Parameterization and Degradation Tracking

Physics-based electrochemical models are useful tools for optimizing battery cell and material design, managing battery use, and understanding physical phenomena, all of which are key in enabling adoption of batteries to electrify transportation, grid storage, and other high carbon emission industries. Fitting these models to experiments can be a useful approach to determine missing parameters that may be difficult to identify experimentally. In this dissertation, two use cases of this approach — model parameterization and degradation tracking — are explored. An introduction to the need for batteries and an overview of challenges in the field is presented in Chapter 1. Of these challenges, those that can be addressed by battery modeling solutions are discussed in further detail. An overview of continuum level physics-based electrochemical models is provided, and the case is made for the utility of parameter estimation. In Chapter 2, an extension of a published model for lithium trivandate cathodes for lithiumion batteries is outlined. While the original model described (de)lithiation and phase change in the cathode, the new model describes simultaneous lithiation of the original phase, lithiation of the newly formed phase, and phase change. Parameters associated with the thermodynamics and kinetics of charge transfer and lithium transport in the second phase are estimated directly from experimental data. This study serves as an example of using the model fitting approach to determine model parameters that would be difficult to isolate and measure experimentally. Chapter 3 explores a similar concept of model parameterization, this time focusing on the electrode tortuosity. Tortuosity is a hard to quantify parameter that describes how tortuous of a path lithium ions must travel through an electrode or separator. Because there are several experimental measurement techniques suggested in the literature that do not always provide consistent results, an approach to fit the tortuosity to a standard rate capability experiment is introduced. The Bayesian approach returns uncertainties in tortuosity estimates, which can be used to predict a range of outcomes for high-rate performance. Covariance between parameters in the model and their impact on uncertainties in tortuosity is also discussed. Beyond model parameterization, parameter estimation can also be useful in the context of tracking degradation by fitting a physics-based model over the course of cycling and interpreting the evolution of the parameter estimates. In Chapter 4, this idea is explored by fitting the model developed in Chapter 2 to cycling of an LVO cell. Parameter estimates are interpreted in conjunction with traditional tear down and electrochemical analysis to identify root causes of degradation for this cell. Depending on the number of parameters being simultaneously estimated, it can become an onerous task to fit model parameters, especially if the physics-based model cannot easily be enclosed in an efficient optimization algorithm. To this end, machine learning (ML) can be useful. If a ML model is trained offline on synthetic data generated by a battery model to map the observable electrochemical data to parameters in the battery model, the ML model can be deployed to estimate parameters from experiment. These models can be referred to as inverse ML models, since they perform the inverse task of a "forward" physics based model. The procedure described above is implemented in Chapter 5. Interpretable ML models are trained on published synthetic data generated by equivalent circuit models. Pseudo-OCV (slow charge, C/25) full cell voltage curves are passed into the inverse ML models to estimate degradation modes in lithium ion batteries and classify which electrode limits cell capacity. These models are useful in diagnosing the state of the battery at any given time. Accuracies of the inverse ML models are evaluated on independent test sets also composed of synthetic data and are published to benchmark future diagnostic studies. The insights derived from the trained ML models in terms of which features in the full cell voltage curves are predictive of the degradation modes are compared to expert insights. In chapter 6, the robustness of the inverse ML approach towards model-experiment disagreement is probed. If the experiment does not directly map onto the protocol used to generate the synthetic training data for the ML model, or if the model itself is inherently a poor descriptor of experiment, the inverse ML model will inevitably return inaccurate estimates. In this chapter, a feed forward neural network (NN) is employed as the inverse ML model. In two case studies of model-experiment disagreement, the NN returns biased parameter estimates. A simple data augmentation procedure is introduced to mitigate these biases. Chapter 7 ties together the understanding developed in the previous chapters by applying more robust neural networks to estimate parameters for LVO cells cycled at different rates. This study demonstrates how to interpret parameter estimates in conjunction with cycling data to gain mechanistic insight into degradation. A complex map of coupled degradation hypotheses is reduced to a smaller subset of possible mechanisms for two exemplary LVO cells, and parameter estimates for a larger set of LVO cells are discussed. The framework presented in this study synergistically combines experiment, physics-based modeling, and machine learning to better understand degradation phenomena

Static Stability and Dynamic Analysis of Barge Foaters for An Offshore Wind Turbine

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Numerical Simulation of Breaking Waves on a Plane Slope with a Parallel Level Set Solver

River, Estuarine and Coastal Dynamic

An Overview of Wave Impact Forces on Offshore Wind Turbine Substructures

AbstractOffshore wind turbines are always subjected to highly varying aerodynamic and hydrodynamic loads which dictate the design phase of the wind turbine substructures. The breaking wave forces yield the highest hydrodynamic loads on substructures in shallow water, particularly plunging breaking waves. Due to the complex and transient nature of the impact forces, the description requires more details concerning the physical properties of breaking waves and the response of the structure. The objective of this paper is to give an overview of the previous and recent research on wave impact forces and the key issues pertaining to these forces on offshore wind turbine substructures

Numerical study of wave transformation using the free surface reconstruction method

The study of irregular wave field is complex due to its random hydrodynamic char- acteristics. Many experimental studies have been performed in the past to study irregular waves. However, numerical investigations are less time consuming and expensive as compared to the experimental studies. For a good validation of the numerical model, it is essential to reproduce the laboratory waves numerically. The reconstruction of the numerical irregular free surface el- evation is necessary because the paddle signal for the wave-maker in experiments is unknown in most of the cases. It is quite challenging to reconstruct the time history of free surface elevation of irregular waves because of the random wave phases and wave periods. In the present work, a numerical investigation is performed using the open-source computational fluid dynamics (CFD) model REEF3D to test and validate the reconstruction of free surface profiles for irregular wave propagation. Two-dimensional irregular waves are generated by super-positioning of the regu- lar wave components. In the current reconstruction approach, the free surface is reconstructed by representing the irregular free surface elevation as a summation of its Fourier components. First, the free surface reconstruction method is tested for irregular waves in a two-dimensional wave tank with constant water depth. The reconstructed free surface elevations shows a good match with the theoretical wave profiles. Further, the method is used to reconstruct the wave transformation over an impermeable fully submerged bar where the complex phenomena such as shoaling and wave breaking occur. The reconstructed numerical free surface elevations along the wave tank are compared with the experimental free surface elevations. The complex phenomena such as shoaling and breaking are represented with reasonable accuracy in the numerical model

Simulation of breaking focused waves over a slope with a cfd based numerical wave tank

Extreme wave conditions are always identified with large-amplitude breaking waves in shallow waters. Focused waves can often be used to describe extreme waves which evolve during the nonlinear wave-wave interaction, occurring at one point in space and time. Under- standing breaking focused waves has many design-related implications for the design of offshore wind turbine (OWT) substructures in shallow waters. The main objective of the paper is to model breaking focused waves over a sloping seabed and study the breaking characteristics us- ing the open-source CFD model REEF3D. The numerical model describes the two-phase flow using the incompressible Reynolds-Averaged Navier-Stokes (RANS) equations together with the continuity equation. The model uses a fifth-order WENO scheme for convection discretization and a third order Runge-Kutta scheme for time discretization along with the level set method to obtain the free surface, yielding accurate wave propagation in the numerical wave tank. Solid boundaries are accounted through the ghost cell immersed boundary method. The free surface is modeled with the level set method. Turbulence is described with the two-equation k −ω model. In the numerical wave tank, the focused waves are generated using a single flap-type maker theory. The numerical results are in good agreement with experimental results for complex free surface elevations measured at several locations along the wave tank. The numerical aspects related to the development of the breaking process are investigated together with the evolution of focusing wave group in the numerical wave tank. Further, the study also examines the free surface flow features that evolve during the breaking process

Analysis of different methods for wave generation and absorption in a CFD-based numerical wave tank

In this paper, the performance of different wave generation and absorption methods in computational fluid dynamics (CFD)-based numerical wave tanks (NWTs) is analyzed. The open-source CFD code REEF3D is used, which solves the Reynolds-averaged Navier-Stokes (RANS) equations to simulate two-phase flow problems. The water surface is computed with the level set method (LSM), and turbulence is modeled with the k-\u3c9 model. The NWT includes different methods to generate and absorb waves: the relaxation method, the Dirichlet-type method and active wave absorption. A sensitivity analysis has been conducted in order to quantify and compare the differences in terms of absorption quality between these methods. A reflection analysis based on an arbitrary number of wave gauges has been adopted to conduct the study. Tests include reflection analysis of linear, second- and fifth-order Stokes waves, solitary waves, cnoidal waves and irregular waves generated in an NWT. Wave breaking over a sloping bed and wave forces on a vertical cylinder are calculated, and the influence of the reflections on the wave breaking location and the wave forces on the cylinder is investigated. In addition, a comparison with another open-source CFD code, OpenFOAM, has been carried out based on published results. Some differences in the calculated quantities depending on the wave generation and absorption method have been observed. The active wave absorption method is seen to be more efficient for long waves, whereas the relaxation method performs better for shorter waves. The relaxation method-based numerical beach generally results in lower reflected waves in the wave tank for most of the cases simulated in this study. The comparably better performance of the relaxation method comes at the cost of larger computational requirements due to the relaxation zones that have to be included in the domain. The reflections in the NWT in REEF3D are generally lower than the published results for reflections using the active wave absorption method in the NWT based on OpenFOAM

A Green Process for Starch Oleate Synthesis by Cryptococcus sp. MTCC 5455 Lipase and Its Potential as an Emulsifying Agent

Starch oleate is synthesized in an aqueous medium using lipase from the yeast Cryptococcus sp. MTCC 5455. The optimum conditions of esterification are found at 24 h and 30 �C with an oleic acid/starch molar ratio of 1:2 using 500U of lipase and the degree of substitution was 0.26. Spectral techniques confirm the presence of oleate group in the modified potato starch. Scanning electron microscopic and X-ray diffraction studies also reveal the morphological and crystallographic properties of starch which are disrupted during the esterification process. Thermogravimetric analysis indicates the decrease in thermal stability of starch oleate due to the transformed structure of starch from semi crystalline to an amorphous form. The synthesized starch oleate could impart 85% stability to emulsions and has potential as an emulsifier in food sector owing to its eco-friendly preparation