Resistive Random Access Memories (RRAMs) are based on resistive switching (RS) operation and exhibit a set of technological features that make them ideal candidates for applications related to non-volatile memories, neuromorphic computing and hardware cryptography. For the full industrial development of these devices different simulation tools and compact models are needed in order to allow computer-aided design, both at the device and circuit levels. Most of the different RRAM models presented so far in the literature deal with temperature effects since the physical mechanisms behind RS are thermally activated; therefore, an exhaustive description of these effects is essential. As far as we know, no revision papers on thermal models have been published yet; and that is why we deal with this issue here. Using the heat equation as the starting point, we describe the details of its numerical solution for a conventional RRAM structure and, later on, present models of different complexity to integrate thermal effects in complete compact models that account for the kinetics of the chemical reactions behind resistive switching and the current calculation. In particular, we have accounted for different conductive filament geometries, operation regimes, filament lateral heat losses, the use of several temperatures to characterize each conductive filament, among other issues. A 3D numerical solution of the heat equation within a complete RRAM simulator was also taken into account. A general memristor model is also formulated accounting for temperature as one of the state variables to describe electron device operation. In addition, to widen the view from different perspectives, we deal with a thermal model contextualized within the quantum point contact formalism. In this manner, the temperature can be accounted for the description of quantum effects in the RRAM charge transport mechanisms. Finally, the thermometry of conducting filaments and the corresponding models considering different dielectric materials are tackled in depth.

We present a method to extract the model parameters of illuminated solar cells based on genetic algorithms. The solar cell model is the well known lumped parameter equivalent circuit containing four components. Its mathematical description has been made explicit using of the Lambert function. We have tested the ability of our method against a direct extraction method to give back some known results. The test consists in fitting I-V characteristics of an organic photovoltaic solar cell obtained from randomly generated parameters. The accuracy and the reliability of the results are much higher, particularly concerning the reverse saturation current. The time needed to perform the extraction of each set of parameter is around 100 s in a PC with a dual core processor, at 3.5 GHz with 2 GB RAM.

Land Surface Temperature (LST) as provided by remote sensing onboard satellites is a key parameter for a number of applications in Earth System studies, such as numerical modelling or regional estimation of surface energy and water fluxes. In the case of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra or Aqua, pixels have resolutions near 1 km ${}^2$ , LST values being an average of the real subpixel variability of LST, which can be significant for heterogeneous terrain. Here, we use Landsat 7 LST decametre-scale fields to evaluate the temporal and spatial variability at the kilometre scale and compare the resulting average values to those provided by MODIS for the same observation time, for the very heterogeneous Campus of the University of the Balearic Islands (Mallorca, Western Mediterranean), with an area of about 1 km ${}^2$ , for a period between 2014 and 2016. Variations of LST between 10 and 20 K are often found at the sub-kilometre scale. In addition, MODIS values are compared to the ground truth for one point in the Campus, as obtained from a four-component net radiometer, and a bias of 3.2 K was found in addition to a Root Mean Square Error (RMSE) of 4.2 K. An indication of a more elaborated local measurement strategy in the Campus is given, using an array of radiometers distributed in the area.