The hydrodesulfurization (HDS) process is widely used in the industry to eliminate sulfur compounds from fuels. However, removing dibenzothiophene (DBT) and its derivatives is a challenge. Here, the key aspects that affect the efficiency of catalysts in the HDS of DBT were investigated using machine learning (ML) algorithms. The conversion of DBT and selectivity was estimated by applying Lasso, Ridge, and Random Forest regression techniques. For the estimation of conversion of DBT, Random Forest and Lasso offer adequate predictions. At the same time, regularized regressions have similar outcomes, which are suitable for selectivity estimations. According to the regression coefficient, the structural parameters are essential predictors for selectivity, highlighting the pore size, and slab length. These properties can connect with aspects like the availability of active sites. The insights gained through ML techniques about the HDS catalysts agree with the interpretations of previous experimental reports.

Compounds containing carbamate moieties and their derivatives can generate serious public health threats and environmental problems due their high potential toxicity. In this study, a quantitative structure–toxicity relationship (QSTR) model has been developed by using one hundred seventy-eight carbamate derivatives whose toxicities in rats (oral administration) have been evaluated. The QSRT model was rigorously validated by using either tested or untested compounds falling within the applicability domain of the model. A structure-based evaluation by docking from a series of carbamates with acetylcholinesterase (AChE) was carried out. The toxicity of carbamates was predicted using physicochemical, structural, and quantum molecular descriptors employing a DFT approach. A statistical treatment was developed; the QSRT model showed a determination coefficient (R²) and a leave-one-out coefficient (Q²_LOO) of 0.6584 and 0.6289, respectively.

In recent years the use of smartphones has increased because the convergence of technologies related in the development of these mobile devices. This convergence relates to communication, handheld devices and applications. The most common development platforms are Android, iOS, Windows Phone and Symbian. In most cases mobile application developers restrict the development to one or two platforms, since there is not strong multi-platform execution support. For example, an iOS application will not run on Android or another system. One solution is web development, but it presents some problems such as adaptability, overload on servers, low use of device capabilities, and poor code reuse. A better solution is to develop a middleware that helps to develop open applications. This paper gives an analysis of the problems involved in the design of open mobile applications, challenges and some solutions.