Likely, many text on MATLAB, C++, FORTRAN and Python programming languages exist in chemical engineering libraries, discussing their applications for chemical engineering numerical analysis. R programming language, which has been in existence for more than 40 years is just evolving as a language of choice for data analytics in science and engineering. Here, it is shown that, numerical analysis with equations of state (EOS), especially the Peng-Robinson EOS, typically taught in undergraduate chemical engineering introductory courses can be solved with a developed or existing R source codes. Out of several other mathematical methods, including Fixed-point iteration, Regula-Falsi, Bisection and their modified/hybrid methods recently developed, only Secant and Newton’s method algorithm were followed to solve a sample problem by writing an R program. Although sufficient, in-depth study of the R language using some recommended manuals in this work can be a guide in implementing a solution with R for other numerical methods, for the same problem, as well as several other existing analytical and statistical chemical engineering problems out there.

Depression is a prevailing mental disturbance affecting an individual’s thinking and mental development. There has been much research demonstrating effective automated prediction and detection of Depression.Many datasets used suffer from class imbalance where samples of a dominant class outnumber the minority class that is to be detected. This review paper uses the PRISMA review methodology to enlist different class imbalance handling techniques used in Depression prediction and detection research. The articles were taken from information technology databases. The research gap was found that under sampling methods were few for predicting and detecting Depression and regression modelling could be considered for future research. The results also revealed that the common data level technique is SMOTE as a single method and the common ensemble method is SMOTE, oversampling and under sampling techniques. The model level consisted of various algorithms that can be used to tackle the class imbalance problem.

In this paper, an investigation was done to identify writing style features that can be used for cross-topic and cross-genre documents in the Authorship Identification task from 2003 to 2015. Different writing style features were empirically evaluated that were previously used in single topic and single genre documents for Authorship Identification to determine whether they can be used effectively for cross-topic and crossgenre Authorship Identification using an ablation process. The dataset used was taken from the 2015 PAN CLEF Forum English collection consisting of 100 sets. Furthermore, it was investigated whether combining some of these feature sets can help improve the authorship identification task. Three different classifiers were used: Naïve Bayes, Support Vector Machine, and Random Forest. The results suggest that a combination of a lexical, syntactical, structural, and content feature set can be used effectively for cross topic and cross genre authorship identification, as it achieved an AUC result of 0.837.

In this paper, an investigation was done to identify writing style features that can be used for cross-topic and cross-genre documents in the Authorship Identification task from 2003 to 2015. Different writing style features were empirically evaluated that were previously used in single topic and single genre documents for Authorship Identification to determine whether they can be used effectively for cross-topic and crossgenre Authorship Identification using an ablation process. The dataset used was taken from the 2015 PAN CLEF Forum English collection consisting of 100 sets. Furthermore, it was investigated whether combining some of these feature sets can help improve the authorship identification task. Three different classifiers were used: Naïve Bayes, Support Vector Machine, and Random Forest. The results suggest that a combination of a lexical, syntactical, structural, and content feature set can be used effectively for cross topic and cross genre authorship identification, as it achieved an AUC result of 0.837.

R is widely used by researchers in the statistics field and academia. In Botswana, it is used in a few research for data analysis. The paper aims to synthesis research conducted in Botswana that has used R programming for data analysis and to demonstrate to data scientists, the R community in Botswana and internationally the gaps and applications in practice in research work using R in the context of Botswana. The paper followed the PRISMA methodology and the articles were taken from information technology databases. The findings show that research conducted in Botswana that use R programming were used in Health Care, Climatology, Conservation and Physical Geography, with R part as the most used R package across the research areas. It was also found that a lot of R packages are used in Health care for genomics, plotting, networking and classification was the common model used across research areas.