Management of Mucopolysaccharidosis Type I in Saudi Arabia: Insights from Saudi Arabia

Mucopolysaccharidosis (MPS) is a group of rare disorders that are characterized by intracellular accumulation of glycosaminoglycans with subsequent cellular and organ dysfunction. In the Middle East, especially Saudi Arabia, higher prevalence of MPS type I was observed compared to reported rates from European countries and the United States (U.S). The present work was developed as a part of the Saudi MPS Group’s efforts to address the current situation of MPS type I in Saudi Arabia and to reach a national consensus in the management of MPS type I. The first “Management of MPS Type I Advisory Board” meeting was held in Riyadh on May 2, 2019, to reflect the expert opinions regarding different aspects of MPS type I and develop this manuscript; eight consultants from different specialties (medical genetics, pediatric rheumatology, and pediatric endocrinology), representing six Saudi institutions, in addition to a global expert in genetics participated in the meeting

Univariate and multivariate analyses of the asset returns using new statistical models and penalized regression techniques

The COVID-19 epidemic has had a profound effect on almost every aspect of daily life, including the financial sector, education, transportation, health care, and so on. Among these sectors, the financial and health sectors are the most affected areas by COVID-19. Modeling and predicting the impact of the COVID-19 epidemic on the financial and health care sectors is particularly important these days. Therefore, this paper has two aims, (i) to introduce a new probability distribution for modeling the financial data set (oil prices data), and (ii) to implement a machine learning approach to predict the oil prices. First, we introduce a new approach for developing new probability distributions for the univariate analysis of the oil price data. The proposed approach is called a new reduced exponential-$X$ (NRE-$X$) family. Based on this approach, two new statistical distributions are introduced for modeling the oil price data and its log returns. Based on certain statistical tools, we observe that the proposed probability distributions are the best competitors for modeling the prices' data sets. Second, we carry out a multivariate analysis while considering some covariates of oil price data. Dual well-known machine learning algorithms, namely, the least absolute shrinkage and absolute deviation (Lasso) and Elastic net (Enet) are utilized to achieve the important features for oil prices based on the best model. The best model is established through forecasting performance

Computational Modeling of Detecting a Randomly Target in a Bounded Known Region

This paper formulates a novel quartile coordinated search technique that finds a 3-D randomly located landmine. In addition we calculated the expected time to detect the landmine in Symmetric Coordinated search Technique (SCST). We introduce the optimal search strategy that minimizes the expected time for detecting the landmine assuming trivarite standard normal distribution in the cases SCST. An approximation algorithm has been introduced to facilitate searching procedures. The stochastic Euclidean distance between a known satellite equations and a randomly moving meteor in the 3 dimensional has been introduced. Also, we illustrate a technique known as coordinated search that completely characterizes the search for a randomly located target on the plane. The idea is to avoid wasting time looking for a missing target. Four searchers or robots start from the center of circle to search out a lost target, an illustrative application from real life has been introduced not only to demonstrate the applicability of this search technique but also to facilitate the using of constrained approaches and expectations

Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model

Because of the complexity, nonlinearity, and volatility, stock market forecasting is either highly difficult or yields very unsatisfactory outcomes when utilizing traditional time series or machine learning techniques. To cope with this problem and improve the complex stock market’s prediction accuracy, we propose a new hybrid novel method that is based on a new version of EMD and a deep learning technique known as long-short memory (LSTM) network. The forecasting precision of the proposed hybrid ensemble method is evaluated using the KSE-100 index of the Pakistan Stock Exchange. Using a new version of EMD that uses the Akima spline interpolation technique instead of cubic spline interpolation, the noisy stock data are first divided into multiple components technically known as intrinsic mode functions (IMFs) varying from high to low frequency and a single monotone residue. The highly correlated sub-components are then used to build the LSTM network. By comparing the proposed hybrid model with a single LSTM and other ensemble models such as the support vector machine (SVM), Random Forest, and Decision Tree, its prediction performance is thoroughly evaluated. Three alternative statistical metrics, namely root means square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), are used to compare the aforementioned techniques. The empirical results show that the suggested hybrid Akima-EMD-LSTM model beats all other models taken into consideration for this study and is therefore recommended as an effective model for the prediction of non-stationary and nonlinear complex financial time series data

Some Properties of Entropy for an Extended Exponential Distribution(EED) Based on Order Statistics

Residual and past residual entropy functions concatenated with uncertainty measurements are predominant factors in information theory. Many existing comparative analysis procedures are already asserted for determining the aging process of associated components like life testing problems and survival function. This paper focuses on proposing functions that are based upon the extended exponential distribution (EED). Existing past residual entropy function is examined on upper bounds of different order statistics and the results are analyzed for proposed Shannon’s entropy and residual entropy functions

Some Properties of Entropy for an Extended Exponential Distribution(EED) Based on Order Statistics

Residual and past residual entropy functions concatenated with uncertainty measurements are predominant factors in information theory. Many existing comparative analysis procedures are already asserted for determining the aging process of associated components like life testing problems and survival function. This paper focuses on proposing functions that are based upon the extended exponential distribution (EED). Existing past residual entropy function is examined on upper bounds of different order statistics and the results are analyzed for proposed Shannon’s entropy and residual entropy functions

Some Properties and Application of Modified Jacknified Liu-Type Negative Binomial Ridge Regression

The aim of this paper is to introduce a new estimator for the modified Jackknified Liu Type Negative Binomial. As in the presence of multicollinearity, the Maximum Likelihood Estimator (MLE) is unable to produce valid statistical inference. So, this paper is designed to solve the problem of multicollinearity. Several ridge regression estimators are used for this purpose. Moreover, Monte Carlo simulation and real life data set are applied on the proposed and existing estimators to evaluate the performance of proposed estimator in the case of MSE. The results reveal that our proposed estimator has best performance among all other estimators (i.e.ML,NBRR,LTNB,JNBR)

Optimal Discrete Search for a Randomly Moving COVID19

In this paper, we subedit a search for a randomly moving Coronavirus (COVID-19) among a finite set of different states. We use a monitoring system to search for COVID-19 which is hidden in one of the n cells of the respiratory system in the human body in each fixed number of time intervals m. The expected rescue time of the patient and detecting COVID-19 has been obtained. Also, we extend the results and obtain the total optimal expected search time of COVID-19. The optimal search strategy is derived suing a dynamic programming algorithm. An illustrative real life example introduced to clear the applicability of this model

Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing

In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecification bias. Selecting a sample by applying the balancing conditions on some functions of the covariates can reduce such bias. This study aims at suggesting an estimator of population total by applying the balancing conditions on the basis functions of the auxiliary character(s) for the situations where the working model is different from the underlying true model under a ranked set sampling without replacement scheme. Special cases of the misspecified basis function model, i.e. homogeneous, linear, and proportional, are considered and balancing conditions are introduced in each case. Both simulation and bootstrapped studies show that the total estimators under proposed sampling mechanism keep up the superiority over simple random sampling in terms of efficiency and maintaining robustness against model failure

On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events

The rising number of confirmed cases and deaths in Pakistan caused by the coronavirus have caused problems in all areas of the country, not just healthcare. For accurate policy making, it is very important to have accurate and efficient predictions of confirmed cases and death counts. In this article, we use a coronavirus dataset that includes the number of deaths, confirmed cases, and recovered cases to test an artificial neural network model and compare it to different univariate time series models. In contrast to the artificial neural network model, we consider five univariate time series models to predict confirmed cases, deaths count, and recovered cases. The considered models are applied to Pakistan’s daily records of confirmed cases, deaths, and recovered cases from 10 March 2020 to 3 July 2020. Two statistical measures are considered to assess the performances of the models. In addition, a statistical test, namely, the Diebold and Mariano test, is implemented to check the accuracy of the mean errors. The results (mean error and statistical test) show that the artificial neural network model is better suited to predict death and recovered coronavirus cases. In addition, the moving average model outperforms all other confirmed case models, while the autoregressive moving average is the second-best model