The Analysis Of Bayesian Bootstrap Binary Logistic Regression In Modeling The Recovery Rate Of Covid-19 Patients

This study applied the Bayesian binary logistic regression method and the Bootstrap method to model the recovery rate of COVID-19 patients. The aim is to model the recovery rate of COVID-19 patients in order to identify the symptoms and factors affecting the recovery rate of COVID-19 patients. Data obtained from the M. Djamil Hospital, Padang City and Andalas University Hospital on COVID-19 patients in West Sumatra in 2020 were used as the case data. The case data were randomly divided with proportion of 80% training data and 20% testing data. The training data with Bayesian binary logistic regression and perform parameter estimation were later analyzed for testing data using the Bootstrap method. The parameter significance results show that there is one predictor variable that significantly affects the recovery rate of COVID-19 patients, namely patients aged 0 – 59 years. The Bayesian binary logistic regression method used in the modeling has been accurate based on the performance test of the algorithm that has been used with the Bootstrap method. This study proves that the estimated value with Bayesian binary logistic regression is at the 95% Bootstrap confidence interval. The results of the classification model for the recovery rate of COVID-19 patients show good performance by producing high accuracy, sensitivity, and precision values in identifying patients. Therefore, it can be concluded that Bayesian binary logistic regression and the Bootstrap method can be used to model the recovery rate of COVID-19 patients as they produce high classification accurac

Optimizing Recovery Conditions in Collegiate Female Soccer Athletes Using Machine Learning

Valparaiso University Women’s Soccer Organization collects heart rate data using Firstbeat Trackers to give players biofeedback on their performance, health, and recovery. However, this information only provides an opaque view into what factors are affecting their recovery. Various studies concur that the intensity of exercise has the greatest influence on recovery; but there is little published research that has analyzed the effects of environmental conditions on athletic activities. Current research shows that temperature influences heart rate variability, a commonly used metric of recovery, in non-physically demanding activities; however, there are no substantial studies that have developed predictive models that pair this with athletic exercise while controlling for intensity. Using the biometrics collected by the activity trackers, multiple machine learning algorithms and statistical methods were implemented to answer two main questions: 1) How does environmental temperature affect players’ recovery? 2) Which machine learning models and measurements of recovery provide a predictive capability that coaches can implement for better decision making? This research compares linear and non-linear modeling methods on team and individual player data and their accuracy and effectiveness of predicting athlete recovery. Models range from transparent linear regressions to black-box convolutional neural networks to find a balance between predictive capability and transparency of interpretation. Initial results show that modeling individual player data using random forest regression gives an accurate view of how factors such as temperature and intensity influence soccer athlete recovery

Application of Response Surface Methodology on Beneficiation of Sudanese Chromite Ore via Pilot Plant Shaking Table Separator

High grade chromite ore has been decreased constantly due to the importance of chromium element in industrial uses such as metallurgical, chemical, and refractory industries. Therefore, beneficiation of low grade has been more significant .Response Surface Methodology (RSM) is combination of statistical and mathematical methods used for modeling and analyzing problems. In this study, the Central Composite Design (CCD) was applied by using Design-Expert (version 6.0.5) for modeling and optimizing the effect of operating variables on the performance of gravity separation via pilot plant shaking table for chromite ore. Three operating variables were studied, namely feed rate, tilt angle, and flow rate during the tests. The sample under study is low grade chromite ore, containing (30.21%, Cr2O3). The mathematical model equations of ANOVA model revealed that the grade of concentrate is more sensetive for feed rate (g/min) compered to water flow rate (l/min).whereas , recovery of concentrate is more sensetive for tilt angle compered to water flow rate (l/min) and feed rate (g/min). Optimized responses for the beneficiation process were found at concentrate with 48.52% Cr2O3 in with 83.09% recovery and it was achieved at water flow rate 15.33 l/min, tilt angle 2.16 ≈ 2.00 degree, and feed rate of 195.38 g/mi

Simulation Study on Water-Alternating-Gas (WAG) Injection with Different Schemes and Types of Gas in a Sandstone Reservoir

Water-Alternating-Gas (WAG) injection is one of the Enhanced Oil Recovery (EOR) techniques applied in oil and gas industry. In a WAG application, there are a lot of combinations of WAG schemes to be selected from. The common stated problem is to determine the optimum WAG schemes for a certain field. Different WAG schemes can be formed by adjusting the WAG parameters, i.e. WAG ratio, WAG injection rate, WAG cycle sizes and etc. Another problem is the ambiguous feasibility of other type of gas in WAG application. The objective of this Final Year Project (FYP) was to simulate and determine the impacts of WAG parameters on the recovery for a sandstone reservoir, and also to evaluate the feasibility of different types of gas in WAG injections. This project was carried out by using a compositional simulator developed by Computer Modeling Group Ltd (CMG). The inputs needed for the simulations were collected from the literatures available. This study focuses on WAG application in a sandstone reservoir. The performance of each scheme was evaluated based primarily on the ultimate recovery. From these outcomes, various WAG schemes and the impacts of each WAG parameter can be compared, and thus deciding the optimum one. It was concluded that WAG ratio, WAG injection rate and types of WAG gas have profound effects on WAG performance, while WAG cycle sizes has insignificant impact on the recovery

High Dimensional Semiparametric Gaussian Copula Graphical Models

In this paper, we propose a semiparametric approach, named nonparanormal skeptic, for efficiently and robustly estimating high dimensional undirected graphical models. To achieve modeling flexibility, we consider Gaussian Copula graphical models (or the nonparanormal) as proposed by Liu et al. (2009). To achieve estimation robustness, we exploit nonparametric rank-based correlation coefficient estimators, including Spearman's rho and Kendall's tau. In high dimensional settings, we prove that the nonparanormal skeptic achieves the optimal parametric rate of convergence in both graph and parameter estimation. This celebrating result suggests that the Gaussian copula graphical models can be used as a safe replacement of the popular Gaussian graphical models, even when the data are truly Gaussian. Besides theoretical analysis, we also conduct thorough numerical simulations to compare different estimators for their graph recovery performance under both ideal and noisy settings. The proposed methods are then applied on a large-scale genomic dataset to illustrate their empirical usefulness. The R language software package huge implementing the proposed methods is available on the Comprehensive R Archive Network: http://cran. r-project.org/.Comment: 34 pages, 10 figures; the Annals of Statistics, 201

Fast Low-Rank Matrix Learning with Nonconvex Regularization

Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance. However, the resultant optimization problem is much more challenging. A very recent state-of-the-art is based on the proximal gradient algorithm. However, it requires an expensive full SVD in each proximal step. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, a cutoff can be derived to automatically threshold the singular values obtained from the proximal operator. This allows the use of power method to approximate the SVD efficiently. Besides, the proximal operator can be reduced to that of a much smaller matrix projected onto this leading subspace. Convergence, with a rate of O(1/T) where T is the number of iterations, can be guaranteed. Extensive experiments are performed on matrix completion and robust principal component analysis. The proposed method achieves significant speedup over the state-of-the-art. Moreover, the matrix solution obtained is more accurate and has a lower rank than that of the traditional nuclear norm regularizer.Comment: Long version of conference paper appeared ICDM 201

A comparison of two-stage segmentation methods for choice-based conjoint data: a simulation study.

Due to the increasing interest in market segmentation in modern marketing research, several methods for dealing with consumer heterogeneity and for revealing market segments have been described in the literature. In this study, the authors compare eight two-stage segmentation methods that aim to uncover consumer segments by classifying subject-specific indicator values. Four different indicators are used as a segmentation basis. The forces, which are subject-aggregated gradient values of the likelihood function, and the dfbetas, an outlier detection measure, are two indicators that express a subject’s effect on the estimation of the aggregate partworths in the conditional logit model. Although the conditional logit model is generally estimated at the aggregate level, this research obtains individual-level partworth estimates for segmentation purposes. The respondents’ raw choices are the final indicator values. The authors classify the indicators by means of cluster analysis and latent class models. The goal of the study is to compare the segmentation performance of the methods with respect to their success rate, membership recovery and segment mean parameter recovery. With regard to the individual-level estimates, the authors obtain poor segmentation results both with cluster and latent class analysis. The cluster methods based on the forces, the dfbetas and the choices yield good and similar results. Classification of the forces and the dfbetas deteriorates with the use of latent class analysis, whereas latent class modeling of the choices outperforms its cluster counterpart.Two-stage segmentation methods; Choice-based conjoint analysis; Conditional logit model; Market segmentation; Latent class analysis;

Understanding reservoir engineering aspects of shale oil development on the Alaska North Slope

Thesis (M.S.) University of Alaska Fairbanks, 2014Horizontal drilling and multi-stage hydraulic fracturing have made the commercial development of nano-darcy shale resources a success. The Shublik shale, a major source rock for hydrocarbon accumulations on the North Slope of Alaska, has huge potential for oil and gas production, with an estimated 463 million barrels of technically recoverable oil. This thesis presents a workflow for proper modeling of flow simulation in shale wells by incorporating results from hydraulic fracturing software into hydraulic fracture flow modeling. The proposed approach allows us to simulate fracture propagation and leak-off of fracturing fluid during hydraulic fracturing. This process honors the real proppant distribution, horizontal and vertical variable fracture conductivity, and presence of fracturing fluid in the fractures and surrounding matrix. Data from the Eagle Ford Shale in Texas was used for this modeling which is believed to be analogous to Alaska's Shublik shale. The performance of a single hydraulic fracture using a black oil model was simulated. Simulation results showed that for the hydraulically fractured zone, the oil recovery factor is 5.8% over thirty years of production, to an assumed economic rate of 200 STB/day. It was found that ignoring flowback overestimated oil recovery by about 17%. Assuming a constant permeability in the hydraulic fracture plane resulted in overestimation of oil recovery by almost 25%. The conductivity of the unpropped zone affected the recovery factor predictions by as much as 10%. For the case investigated, about 25% of the fracturing fluid was recovered during the first 2 months of production; in total, 44% of it was recovered over thirty years. Permeability anisotropy was found to have a significant effect on the results. These results suggest that assuming a constant conductivity for the fractures and ignoring the presence of water in the fractures and the surrounding matrix leads to overestimation of initial production rates and final recovery factors. In addition, the modified workflow developed here more accurately and seamlessly integrates the modeled induced fracture characteristics in the reservoir simulation of shale resource plays