Prediction of Gas Consumption During Hydrate Formation With or Without the Presence of Inhibitors in a Batch System Using the Esmaeilzadeh-Roshanfekr Equation of State

In this work, the ability of different equations of state to predict the gas consumption during hydrate formation in a batch system has been evaluated using the model of Kashchiev and Firoozabadi. The first state equation used for this purpose was the one developed by Esmaeilzadeh and Roshanfekr. The predictions were then extended using PR, SRK and Patel Teja equations. The ability of the different equations of state were evaluated for single gases of methane and ethane and their mixtures adding to more than a thousand experimental data existing in the literature. The consumption of gas during hydrate formation was predicted both with and without the presence of kinetic inhibitors. In the case of double hydrate formation, the state equation based on the Kashchiev and Firoozabadi model for simple gas was modified by lumping the component of hydrate formation as a pseudocomponent. The results of this extension study show that the equation developed by Esmaeilzadeh and Roshanfekr is just as suitable for predicting gas consumption during hydrate formation as any of the other well known state equations such as PR and SRK

Impacts of DEM Type and Resolution on Deep Learning-Based Flood Inundation Mapping

This paper presents a comprehensive study focusing on the influence of DEM type and spatial resolution on the accuracy of flood inundation prediction. The research employs a state-of-the-art deep learning method using a 1D convolutional neural network (CNN). The CNN-based method employs training input data in the form of synthetic hydrographs, along with target data represented by water depth obtained utilizing a 2D hydrodynamic model, LISFLOOD-FP. The performance of the trained CNN models is then evaluated and compared with the observed flood event. This study examines the use of digital surface models (DSMs) and digital terrain models (DTMs) derived from a LIDAR-based 1m DTM, with resolutions ranging from 15 to 30 meters. The proposed methodology is implemented and evaluated in a well-established benchmark location in Carlisle, UK. The paper also discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. The study found that DTM performs better than DSM at lower resolutions. Using a 30m DTM improved flood depth prediction accuracy by about 21% during the peak stage. Increasing the resolution to 15m increased RMSE and overlap index by at least 50% and 20% across all flood phases. The study demonstrates that while coarser resolution may impact the accuracy of the CNN model, it remains a viable option for rapid flood prediction compared to hydrodynamic modeling approaches

Ensemble Kalman Methods With Constraints

Ensemble Kalman methods constitute an increasingly important tool in both state and parameter estimation problems. Their popularity stems from the derivative-free nature of the methodology which may be readily applied when computer code is available for the underlying state-space dynamics (for state estimation) or for the parameter-to-observable map (for parameter estimation). There are many applications in which it is desirable to enforce prior information in the form of equality or inequality constraints on the state or parameter. This paper establishes a general framework for doing so, describing a widely applicable methodology, a theory which justifies the methodology, and a set of numerical experiments exemplifying it

Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification

Attribution methods are an easy to use tool for investigating and validating machine learning models. Multiple methods have been suggested in the literature and it is not yet clear which method is most suitable for a given task. In this study, we tested the robustness of four attribution methods, namely gradient*input, guided backpropagation, layer-wise relevance propagation and occlusion, for the task of Alzheimer's disease classification. We have repeatedly trained a convolutional neural network (CNN) with identical training settings in order to separate structural MRI data of patients with Alzheimer's disease and healthy controls. Afterwards, we produced attribution maps for each subject in the test data and quantitatively compared them across models and attribution methods. We show that visual comparison is not sufficient and that some widely used attribution methods produce highly inconsistent outcomes

GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI

Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer's Disease (AD). However, PET imaging can be prohibitive in terms of cost and planning, and is also among the imaging techniques with the highest dosage of radiation. Magnetic Resonance Imaging (MRI), in contrast, is more widely available and provides more flexibility when setting the desired image resolution. Unfortunately, the diagnosis of AD using MRI is difficult due to the very subtle physiological differences between healthy and AD subjects visible on MRI. As a result, many attempts have been made to synthesize PET images from MR images using generative adversarial networks (GANs) in the interest of enabling the diagnosis of AD from MR. Existing work on PET synthesis from MRI has largely focused on Conditional GANs, where MR images are used to generate PET images and subsequently used for AD diagnosis. There is no end-to-end training goal. This paper proposes an alternative approach to the aforementioned, where AD diagnosis is incorporated in the GAN training objective to achieve the best AD classification performance. Different GAN lossesare fine-tuned based on the discriminator performance, and the overall training is stabilized. The proposed network architecture and training regime show state-of-the-art performance for three- and four- class AD classification tasks.Comment: Accepted for publication at the MICCAI 2020 conferenc

Decomposition of Socioeconomic Inequality in Catastrophic Health Expenditure: An Evidence from Iran

Background: Evidences showed that the incidence of catastrophic health expenditure is unequally distributed among disadvantaged populations. The present study has tried to explain the contributors of this unfair inequality in Hamadan, Iran. Methods: The target population was households that utilized inpatient services in hospitals of Hamadan. A proportional stratified random sampling method was used to determine study sample (N = 770). The associated factors of catastrophic health expenditure were estimated using logistic regression analysis. The inequality of catastrophic health expenditure was measured by concentration index and explained by decomposition analysis. The data were analyzed by using STATA version 12. Results: The key determinants of catastrophic health expenditure were poor economic status, lower household size, lack of supplementary insurance and the number of hospitalizations. The overall concentration index of catastrophic health expenditure in Hamadan was �0.163 (95 CI: �0.242 to �0.083). Household economic status (63.60) and household size (39.90) were considered as the first and the second largest contributors of catastrophic health expenditure inequality, respectively. Conclusion: It is demonstrated that catastrophic health expenditure inequality in Iran could be explained by the factors beyond the health sector scope. Hence, future policy efforts need to consider both health system factors and the factors beyond the health system to eliminate catastrophic health spending burden and its inequality. © 2019 INDIACLE

Concern for information privacy:a cross-nation study of the United Kingdom and South Africa

Individuals have differing levels of information privacy concern, formed by their expectations and the confidence they have that organisations meet this in practice. Variance in privacy laws and national factors may also play a role. This study analyses individuals’ information privacy expectation and confidence across two nations, the United Kingdom and South Africa, through a survey of 1463 respondents. The findings indicate that the expectation for privacy in both countries are very high. However, numerous significant differences exist between expectations and confidence when examining privacy principles. The overall results for both countries show that there is a gap in terms of the privacy expectations of respondents compared to the confidence they have in whether organisations are meeting their expectations. Governments, regulators, and organisations with an online presence need to consider individuals’ expectations and ensure that controls that meet regulatory requirements, as well as expectations, are in place