BUSCADOR RECOLECTA

Juegos infantiles. Travesuras. La escuela. Discoteca. Fiestas. Bebían vino en la “boiga” de los padres. Rutina habitual de los jóvenes., Jogos de crianças. Travessuras. A escola. Discoteca. Festas. Bebiam vinho na boiga dos pais. Rotina habitual dos jovens., Children's games. Mischief. The school. Disco. Parties. They drank wine in the parents' "boiga". Usual routine of the young people.

This dataset contains the MATHEMATICA code used to implement the linear stability analysis developed in Anil Kumar et al.

Project developed by Giovanni Federico (New York University Abu Dhabi) and Antonio Tena Junguito (Universidad Carlos III de Madrid). Dataset: Austria-Hungary

Machine learning (ML) algorithms offer quick and accurate predictions of material properties at a low computational cost. In this project, the mechanical properties of multiscale poly(3-hydroxybutyrate) (P3HB)-based nanocomposites reinforced with different concentrations of multiwalled carbon nanotubes (MWCNTs), WS2 nanosheets and sepiolite (SEP) nanoclay have been predicted. The nanocomposites were prepared via solution casting. SEM images revealed that the three nanofillers were homogenously and randomly dispersed into the matrix. A synergistic reinforcement effect was attained, resulting in an unprecedented stiffness improvement of 132% upon addition of 1:2:2 wt% SEP:MWCNTs:WS2. Conversely, the increments in strength were only moderates (up to 13.4%). A beneficial effect in the matrix ductility was also found due to the presence of both nanofillers. Four ML approaches, Recurrent Neural Network (RNN), RNN with Levenberg's algorithm (RNN-LV), decision tree (DT) and Random Forest (RF), were applied. The correlation coefficient (R2), mean absolute error (MAE) and mean square error (MSE) were used as statistical indicators to compare their performance. The developed models represent a powerful tool for the optimization of the mechanical properties in multiscale hybrid polymer nanocomposites, saving time and resources in the experimental characterization process.

Project developed by Christopher Absell (University Gothenburg and Instituto Figuerola) Giovanni Federico (New York University Dubai) and Antonio Tena Junguito (Universidad Carlos III de Madrid and Instituto Figuerola). Dataset: Thailand

El ganado (vacas, ovejas, cabras, cerdos). Ferias de ganado. Lana. Lácteos y derivados. La matanza., O gado (vacas, ovelhas, cabras, porcos). Feiras de gado. Lã. Laticínios e derivados. Matança., The cattle (cows, sheep, goats, pigs). Cattle fairs. Wool. Milk and derivatives. The slaughter.

La agricultura era pobre. Faltaban abonos y minerales. La mies y las tareas de siega, recogida y preparación de cereales. El molino, donde estaba instalada una turbina; la molienda. Pan y hornos. Referencias esporádicas al ganado y a tipos de terreno., A agricultura era pobre. Não havia fertilizantes e minerais. A colheita e as tarefas de ceifa, recolha e preparação de cereais. O moinho, onde uma turbina foi instalada; a moagem. Pão e fornos. Referências esporádicas aos tipos de gado e terra., Agriculture was poor. There were no fertilizers and minerals. The harvest and the tasks of mowing, collecting and preparing cereals. The mill, where a turbine was installed; the grinding. Bread and ovens. Sporadic references to livestock and land types.

Conjunto de datos a los que se hace referencia en la monografía: Carlos Petit (editor), Derecho Ex Cathedra 1847-1936. Diccionario de catedráticos españoles, Madrid: Universidad Carlos III de Madrid - Dykinson, 2019. Pograma Historia del Derecho, 76

Project developed by Giovanni Federico (New York University Abu Dhabi) and Antonio Tena Junguito (Universidad Carlos III de Madrid). Dataset: Ceylon (Sri Lanka)