SUMMARYPrincipal component analysis (PCA) is a dimension-reducing tool that replaces the variables in a multivariate data set by a smaller number of derived variables. Dimension reduction is often undertaken to help in interpreting the data set but, as each principal component usually involves all the original variables, interpretation of a PCA can still be difficult. One way to overcome this difficulty is to select a subset of the original variables and use this subset to approximate the principal components. This article reviews a number of techniques for choosing subsets of the variables and examines their merits in terms of preserving the information in the PCA, and in aiding interpretation of the main sources of variation in the data.

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The library environment has drastically changed since 1992 when Bostick's Library Anxiety Scale was developed. This project aimed to develop a scale specifically for undergraduate students. A three-stage study was conducted, using students of Kuwait University. A variety of statistical measures, including factor analysis, were used to process the data. A test re-test was undertaken to estimate the reliability of the scale. The resulting scale, named AQAK, consists of 40 statements clustered into five factors which are: (1) Library resources, (2) Library staff, (3) User knowledge, (4) Library environment, and (5) User education. This new scale with a Cronbach's alpha value of 0.904 is 90 percent reliable. The gender of the participants, the type of high school attended, and the college where they are studying have no relationship with library anxiety.

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Revisiting the classical model by Ross and Kermack-McKendrick, the Susceptible–Infectious–Recovered (SIR) model used to formalize the COVID-19 epidemic, requires improvements which will be the subject of this article. The heterogeneity in the age of the populations concerned leads to considering models in age groups with specific susceptibilities, which makes the prediction problem more difficult. Basically, there are three age groups of interest which are, respectively, 0–19 years, 20–64 years, and >64 years, but in this article, we only consider two (20–64 years and >64 years) age groups because the group 0–19 years is widely seen as being less infected by the virus since this age group had a low infection rate throughout the pandemic era of this study, especially the countries under consideration. In this article, we proposed a new mathematical age-dependent (Susceptible–Infectious–Goneanewsusceptible–Recovered (SIGR)) model for the COVID-19 outbreak and performed some mathematical analyses by showing the positivity, boundedness, stability, existence, and uniqueness of the solution. We performed numerical simulations of the model with parameters from Kuwait, France, and Cameroon. We discuss the role of these different parameters used in the model; namely, vaccination on the epidemic dynamics. We open a new perspective of improving an age-dependent model and its application to observed data and parameters.