Acid‐catalyzed isobutane–isobutylene alkylation in liquid carbon dioxide solution

Liquid carbon dioxide was studied as a solvent for the isobutane–isobutylene alkylation. The acid catalysts in the reaction were anhydrous HF (AHF), pyridinium–poly(hydrogen fluoride) complex (PPHF), concentrated sulfuric acid and trifluoromethanesulfonic acid (TFSA). The effect of the acid–hydrocarbon volume ratio, temperature and residence time on the alkylate quality were studied over the temperature range of 50 T 0 °C. Carbon dioxide as a competing weak base decreases the acidity of the system which parallels the alkylate quality. In the case of HF and TFSA catalysts, solvent CO₂ increased the octane number of the alkylate product (RON 95.6 for HF and 88.0 for TFSAcatalyzed alkylation with CO₂ solvent).     

openModeller: a generic approach to species’ potential distribution modelling

Species’ potential distribution modelling is the process of building a representation of the fundamental ecological requirements for a species and extrapolating these requirements into a geographical region. The importance of being able to predict the distribution of species is currently highlighted by issues like global climate change, public health problems caused by disease vectors, anthropogenic impacts that can lead to massive species extinction, among other challenges. There are several computational approaches that can be used to generate potential distribution models, each achieving optimal results under different conditions. However, the existing software packages available for this purpose typically implement a single algorithm, and each software package presents a new learning curve to the user. Whenever new software is developed for species’ potential distribution modelling, significant duplication of effort results because many feature requirements are shared between the different packages. Additionally, data preparation and comparison between algorithms becomes difficult when using separate software applications, since each application has different data input and output capabilities. This paper describes a generic approach for building a single computing framework capable of handling different data formats and multiple algorithms that can be used in potential distribution modelling. The ideas described in this paper have been implemented in a free and open source software package called openModeller. The main concepts of species’ potential distribution modelling are also explained and an example use case illustrates potential distribution maps generated by the framework.     

Comparing machine learning classifiers in potential distribution modelling

Species’ potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species’ potential distribution.     

A computer program for calculating kappa: application to interexaminer agreement in periodontal research

The kappa statistic is a frequently used measure of interobserver agreement when two or more observers are asked to rate the same items or subjects on some criterion. The advantage of the kappa statistic over simple agreement is that it corrects for agreement by chance. In dental research, because multiple examiners may be involved in assessing subject variables such as attachment loss, bleeding on probing, or periodontal pocket depth, some statistical measure of agreement is needed. The program described here was developed for estimating agreement among periodontal examiners, but also could be used in clinical teaching applications. Assumptions and limitations of the kappa statistic are discussed. Examples of outputs illustrate applications of the program.