The purpose of this study is to explore the influence of factors on patients with chronic kidney disease (CKD) and to establish predictive models using machine learning methods. Data were collected from the Affiliated Hospital of Nanjing University of Chinese Medicine between January 2016 and December 2017, including 69 CKD patients and 155 healthy subjects. This study found that carotid intima-media thickness (cIMT) is the most important indicator among the top 9 important features of each model. In order to find the best model to diagnosis CKD, extreme gradient boosting (XGBoost), support vector machine (SVM) and logistic regression are established and XGBoost is the most suitable model for this study (accuracy, 0.93; specificity, 0.89; sensitivity, 0.94; F1 score, 0.91; AUC, 0.99).

Out of the top ten current global issues, climate change and pollution top the list. These issues have brought about adverse effects on our climate, health and communities. This study aims to investigate the structural performance of sawdust ash blended steel slag aggregate concrete and modelling their structural properties using a multivariate interpolation method. In order to achieve this, the physical properties, physio-chemical, chemical composition, mechanical properties tests were conducted. The result revealed that sawdust ash is classified as a class C type pozzolan having a total of 61.59% combined percentage masses of silica, alumina and ferric oxides, while steel slag aggregate is classified as poorly graded. The composite concrete recorded higher density, compressive and split tensile strengths when compared with normal concrete cured in potable water. The results revealed that normal concrete with normal aggregate is more durable than sawdust ash blended steel slag aggregate (composite) concrete when cured in an aggressive environment. The developed models were found to agree strongly with the experimental data, with an outstanding correlation level. This research has led to the creation of high strength pozzolan blended steel slag aggregate concrete, thus improving waste management, reduction in environmental pollution and \(CO_2\) gas emission.

Collocation methods are efficient approximate methods developed by utilizing suitable set of functions known as trial or basis functions. These methods are used for solving differential equations, integral equations and integro-differential equations, etc. In this study, the Laguerre polynomial of degree 10 is used as a basis function to propose a collocation method for solving higher order linear ordinary differential equations. Four examples on \(4th\), \(6th\), \(8th\) and \(10th\) order ordinary differential equations are selected to illustrate the effectiveness of the method. The numerical results show that the proposed collocation method is easy and straightforward to implement, nevertheless, it is very accurate.

In this paper, a new forms of extended hypergeometric functions are introduced. Some functional relations, integral representations and transformation formulas for these functions are derived.