Oil Prices, Emission Permits Trade of Carbon, and the Dependence Between Their Quantiles

The relations between carbon and oil market is concerned by many scholars but little research has focused on the dependence between their quantiles. We use Quantile on Quantile Regression method to study the impact of WTI crude oil price and Daqing crude oil price on carbon price and use wavelet analysis to clean and decompose the time series. Results show that the impact of crude oil on carbon is heterogeneous. Research based on the original sequence shows that crude oil price has a positive impact on carbon price at all quantile levels. Research based on decomposition sequence shows that the positive impact of crude oil on carbon begins to weaken, the zero effect begins to increase, and the negative impact also begins to appear. However, the negative impact on carbon price becomes stronger with the stability of the time series data obtained from the decomposition of crude oil price series gradually improving, while the positive impact gradually weakens.

Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm

In this chapter, the authors propose a novel statistical model with a residual correction of downscaling coarse precipitation TRMM 3B43 product. The presented study was carried out over Morocco, and the objective is to improve statistical downscaling for TRMM 3B43 products using a machine learning algorithm. Indeed, the statistical model is based on the Transformed Soil Adjusted Vegetation Index (TSAVI), elevation, and distance from the sea. TSAVI was retrieved using the quantile regression method. Stepwise regression was implemented with the minimization of the Akaike information criterion and Mallows' Cp indicator. The model validation is performed using ten in-situ measurements from rain gauge stations (the most available data). The result shows that the model presents the best fit of the TRMM 3B43 product and good accuracy on estimating precipitation at 1km according to 𝑅2, RMSE, bias, and MAE. In addition, TSAVI improved the model accuracy in the humid bioclimatic stage and in the Saharan region to some extent due to its capacity to reduce soil brightness.

Modeling Length of Hospital Stay for Patients With COVID-19 in West Sumatra Using Quantile Regression Approach

This study aims to construct the model for the length of hospital stay for patients with COVID-19 using quantile regression and Bayesian quantile approaches. The quantile regression models the relationship at any point of the conditional distribution of the dependent variable on several independent variables. The Bayesian quantile regression combines the concept of quantile analysis into the Bayesian approach. In the Bayesian approach, the Asymmetric Laplace Distribution (ALD) distribution is used to form the likelihood function as the basis for formulating the posterior distribution. All 688 patients with COVID-19 treated in M. Djamil Hospital and Universitas Andalas Hospital in Padang City between March-July 2020 were used in this study. This study found that the Bayesian quantile regression method results in a smaller 95% confidence interval and higher value than the quantile regression method. It is concluded that the Bayesian quantile regression method tends to yield a better model than the quantile method. Based on the Bayesian quantile regression method, it investigates that the length of hospital stay for patients with COVID-19 in West Sumatra is significantly influenced by Age, Diagnoses status, and Discharge status.

Monte Carlo Simulation in Quantile Regression Parameter for Sparsity Estimate

Monte Carlo is a method used to generate data according to the distribution and resampling until the parameters of the method used became convergen. The purpose of this simulation is first to prove that quantile regression with the estimated sparsity function parameter can model the data according to the non-uniform distribution of the data. Secondly, it’s to prove that the quantile regression is a developed method from the linear regression. The pattern of data which is not uniform is generally referred to as heterogeneous data, while the pattern of uniform data distribution is called homogeneous data. Data in this study will be generated for small and large samples on homogeneous and heterogeneous data. Uniformity of variance will be carried out on both heterogeneous and homogeneous data types, namely 0.25,1 and 4. The parameter estimation process and data generation will be resampled 1000 times. Thus, in conclusion of the simulation studies was the parameter estimates in the classical regression will be the same as the parameter estimates in the quantile regression at quantile 0.5. In the simulation, it is decided that the quantile regression method can be used on heterogeneous and homogeneous data to the unconstrained number of samples and variances.

Reference Values for Serum Lipid Profiles in Iranian Adults: A Spline-Based Quantile Regression Method

Background: Reference measurements are used to screen for abnormal blood lipids. The problem is that these reference values obtained in one population cannot be effective for another population. This study aimed to determine the reference values for blood lipids profiles in the population aged 25-64 years in Yazd. Methods: This descriptive study was based on the data of Yazd Health Study (YaHS) on 3800 adults by cluster sampling. The data set included gender, age, total cholesterol (TC), triglyceride (TG), low-density lipoprotein-cholesterol (LDL-C), and high-density lipoprotein-cholesterol (HDL-C). The linear percentile regression model and the generalized additive model for location, scale, and shape (GAMLSS) were fitted to the data and the reference values were predicted according to the regression coefficients. R-3.0.1 software was used for data analysis. Results: Refrence values for TC, LDL-C, and HDL-C were 109.43-275.72, 45.58-177.70, and 29.95-62.22 mg/dl. The trend of TC, TG, and LDL-C levels increased with age in both genders, but the trend of HDL-C in men decreased with age and remained almost constant in women. Conclusion: In this study, for the population of Yazd, reference values for blood lipids were different in both genders and age groups. Reference values for lipid profile increased in men and women with age.These findings can be used in both prevention and clinical decisions.

Increase in rear-end collision risk by acute stress-induced fatigue in on-road truck driving

Increasing road crashes related to occupational drivers’ deteriorating health has become a social problem. To prevent road crashes, warnings and predictions of increased crash risk based on drivers’ conditions are important. However, in on-road driving, the relationship between drivers’ physiological condition and crash risk remains unclear due to difficulties in the simultaneous measurement of both. This study aimed to elucidate the relationship between drivers’ physiological condition assessed by autonomic nerve function (ANF) and an indicator of rear-end collision risk in on-road driving. Data from 20 male truck drivers (mean ± SD, 49.0±8.2 years; range, 35–63 years) were analyzed. Over a period of approximately three months, drivers’ working behavior data, such as automotive sensor data, and their ANF data were collected during their working shift. Using the gradient boosting decision tree method, a rear-end collision risk index was developed based on the working behavior data, which enabled continuous risk quantification. Using the developed risk index and drivers’ ANF data, effects of their physiological condition on risk were analyzed employing a logistic quantile regression method, which provides wider information on the effects of the explanatory variables, after hierarchical model selection. Our results revealed that in on-road driving, activation of sympathetic nerve activity and inhibition of parasympathetic nerve activity increased each quantile of the rear-end collision risk index. The findings suggest that acute stress-induced drivers’ fatigue increases rear-end collision risk. Hence, in on-road driving, drivers’ physiological condition monitoring and ANF-based stress warning and relief system can contribute to promoting the prevention of rear-end truck collisions.

Regional Agroclimate Characteristic and Its Multiple Teleconnections: A Case Study in the Jianghan Plain (JHP) Region

Agricultural production depends on local agroclimatic conditions to a great extent, affected by ENSO and other ocean-atmospheric climate modes. This paper analyzed the spatio-temporal distributions of climate elements in the Jianghan Plain (JHP), Central China, and explored the impacts from teleconnection patterns, aimed at providing references for dealing with climate change and guiding agricultural activities. Both linear and multifactorial regression models were constructed based on the frequentist quantile regression and Bayesian quantile regression method, with the daily meteorological data sets of 17 national stations in the plain and teleconnection climate characteristic indices. The results showed that precipitation in JHP had stronger spatial variability than evapotranspiration. El Niño probably induced less precipitation in summer while the weakening Arctic Oscillation might lead to more summertime precipitation. The Nash-Sutcliffe efficiency (NSE) of the multifactorial and linear regression model at the median level were 0.42–0.56 and 0.12–0.18, respectively. The mean relative error (MRE) ranged −2.95–−0.26% and −7.83–0.94%, respectively, indicating the much better fitting accuracy of the multiple climatic factors model. Meanwhile it confirmed that the agricultural climate in JHP was under the influence from multiple teleconnection patterns.

SMART ALGORITHM FOR INTERVAL ESTIMATION OF ARC-QUENCHING REACTOR PARAMETERS

The purpose of the arc suppression reactor is to reduce the capacitive current of the network to a safe level where the single-phase earth fault current at the fault location does not exceed five amperes. The current reduced to a permissible level prevents open arcing at the fault location. For proper operation of this device, the arc suppression reactor control automatics needs to adjust the zero-sequence circuit to resonance, which balances the capacitive current of the mains and the inductive current of the reactor. To perform this tuning, it is not necessary to have information about the absolute values of the parameters of the zero-sequence circuit, but by determining them, the automation device is able to solve a wider range of tasks related to network diagnostics and increasing the efficiency of the arc suppression reactor. In this article we consider an approach to solving the problem of parametric identification of arc suppression reactor using the method of interval estimation of object parameters. The information about the operation modes of the arc suppression reactor is obtained by means of a simulation model of the object. Using the observed values, the object parameters are obtained by use of the inverse function to the simulation model. The dependence of the object parameters on the observed parameters is approximated using upper and lower parameter estimation models. The quantile regression method was applied to tune the estimation models. The need to increase the generalization ability of the algorithm is revealed. The method of adjustment of parameters of regularization of learning process to increase generalization ability of algorithm without increase of informativity of data in a training sample is offered. The results of algorithm performance are presented on the example of magnetization branch parameters estimation of arc suppression reactor. The boundaries of the interval of equivalent magnetic core loss resistance and magnetizing inductance are obtained. The limitations of the methods are analyzed, and recommendations for improving the quality of the algorithms are given.

Internal Migrations as a Driving Force of Regional Disintegration: An Empirical Analysis of NUTS-2 Regions in Turkey

Internal migrations, which involve population movements within the borders of a country for economic, political or social reasons, is seen as both a cause and a result of regional imbalances. In this framework, the effect increasing internal migrations have on developed and underdeveloped regions may differ through the effect of the different socio-cultural and economic conditions between regions. The aspect of imbalance is directly related to the extent to which migration affects parameters such as wage, production, consumption, human capital levels, entrepreneurial migration, unemployment, and household income in regions with different stages of development. This study analyzes the effect internal migration has on regional imbalances in Turkey’s NUTS-2 regions during 2008-2019 using the bootstrap quantile regression method. According to the analysis findings, internal migration increases growth in all NUTS-2 regions, but this effect is stronger at higher income levels. In this context, as a region’s income levels increase, the effect of net migration on growth also increases. When considering the migration direction to be from low-income regions to high-income regions, internal migration has been found to increase interregional disintegration in Turkey.

The Nonlinear Links Between Urbanization And CO2: Evidence From Unconditional Quantile And Threshold Regression

The extant literature has provided empirical evidences about the relationship between urbanization and environment, however, a little attention has been paid to non-linear relationship among them. This study aims to measure the effects of urbanization on carbon dioxide emission using quantile and threshold regression method. To this end, the study employed threshold analysis and quantile regression method and analyzed the variation of such non-linearity for different levels of carbon dioxide using quantile regression. The results illustrate that a single threshold and two regimes exist and the threshold for urbanization is 29.56%. Across both regimes, the elasticity estimates form an inverted U-shape impact of urbanization on the carbon dioxide emission. The increase in the marginal effect of urbanization on carbon dioxide emissions up to the median level and a declining trend after this level implies that environmental quality significantly improves for emerging country.