The present work develops an accurate prediction model of the COVID-19 pandemic, capable not only of fitting data with a high regression coefficient but also to predict the overall infections and the infection peak day as well. The model is based on the Verhulst equation, which has been used to fit the data of the COVID-19 spread in China, Italy, and Spain. This model has been used to predict both the infection peak day, and the total infected people in Italy and Spain. With this prediction model, the overall infections, the infection peak, and date can accurately be predicted one week before they occur. According to the study, the infection peak took place on 23 March in Italy, and on 29 March in Spain. Moreover, the influence of the total and partial lockdowns has been studied, without finding any meaningful difference in the disease spread. However, the infected population, and the rate of new infections at the start of the lockdown, seem to play an important role in the infection spread. The developed model is not only an important tool to predict the disease spread, but also gives some significant clues about the main factors that affect to the COVID-19 spread, and quantifies the effects of partial and total lockdowns as well.

A collaborative tool for robotic polishing is developed in this work in order to allow the simultaneous operation of the robot system and human operator to cooperatively carry out the polishing task. For this purpose, the collaborative environment is detailed and the polishing application is designed. Moreover, the polishing tool is developed and its implementation using the minimum viable product approach is obtained. Furthermore, a robust hybrid position-force control is proposed to use the developed tool attached to a robot system and some experiments are given to show its performance.

Composite materials are widely used in the industry for their physical and mechanical properties. The objective of this work is the characterization of some composite materials with natural fibers, in order to determine the suitability in a later use in automotive components. For this purpose, we intend to obtain a model of tensile tests using finite element software, in order to be able to dimension real components.

To date, PET (polyethylene terephthalate) is the most widely used plastic in packaging and also one of the most recycled polymers worldwide. However, the high transport costs and stagnated prices of recycled PET undermine recycling process profits. Transport costs can lower through compaction, which is still not a completely well-known process. Due to heterogeneous designs, the output density of the compaction process varies. This poses problems during equipment design, selection or operation processes as recovery costs sharply increase if the required density is not met. In this manuscript, the authors develop a constitutive model for the compaction of recovered PET packaging. This experimentally validated model, based on the elasto-plastic behaviour of PET packages, allows the output density range to be predicted according to the compression pressure during PET compaction. Unlike other generic compaction models that need more than two parameters, this model uses only one and better correlates with the experimental results. Unlike existing generic models, the model parameters have a physical meaning, which allows the influence of different factors on the compaction process to be assessed. Finally as a result of the model analysis, we provide some tips to enhance compaction equipment efficiency.