In this paper, we revisit the q-state clock model for small systems. We present results for the thermodynamics of the q-state clock model for values from q = 2 to q = 20 for small square lattices of L × L , with L ranging from L = 3 to L = 64 with free-boundary conditions. Energy, specific heat, entropy, and magnetization were measured. We found that the Berezinskii–Kosterlitz–Thouless (BKT)-like transition appears for q > 5, regardless of lattice size, while this transition at q = 5 is lost for L < 10; for q ≤ 4, the BKT transition is never present. We present the phase diagram in terms of q that shows the transition from the ferromagnetic (FM) to the paramagnetic (PM) phases at the critical temperature T 1 for small systems, and the transition changes such that it is from the FM to the BKT phase for larger systems, while a second phase transition between the BKT and the PM phases occurs at T 2. We also show that the magnetic phases are well characterized by the two-dimensional (2D) distribution of the magnetization values. We made use of this opportunity to carry out an information theory analysis of the time series obtained from Monte Carlo simulations. In particular, we calculated the phenomenological mutability and diversity functions. Diversity characterizes the phase transitions, but the phases are less detectable as q increases. Free boundary conditions were used to better mimic the reality of small systems (far from any thermodynamic limit). The role of size is discussed.

Las Redes Neuronales Informadas por la Física (PINNs) se han aplicado con éxito a la resolución de Ecuaciones Diferenciales Parciales (PDEs), abriendo nuevas posibilidades en la computación científica. En este trabajo se propone una metodología basada en PINNs para resolver la Ecuación de Poisson-Boltzmann (PB), aplicada a moléculas de distintos tamaños bajo el modelo de solvente implícito. Se revisaron formulaciones de PB compatibles con PINNs, arquitecturas de red adecuadas y factores que inciden en la minimización. Los resultados muestran buenos niveles de precisión en la energía de solvatación y el potencial de reacción, con errores del orden de 10⁻³. La implementación efectiva requiere regularización de la ecuación y el uso de redes separadas por subdominios (soluto y solvente), además de una representación precisa de la geometría molecular mediante mallas finas. Se identificaron desafíos al incorporar integrales basadas en datos experimentales en la función de pérdida. Como resultado, se desarrolló la librería XPPBE, orientada a facilitar el uso de esta metodología por parte de la comunidad científica.

Feature descriptors in histopathological images pose a significant challenge for the implementation of Content-Based Image Retrieval (CBIR) systems, which are essential tools for assisting pathologists. This complexity arises from the diverse types of tissues and the high dimensionality of Whole Slide Images (WSIs). Deep learning models such as Convolutional Neural Networks (CNNs) and Vision Transformers have improved the extraction of these feature descriptors. These models typically generate embeddings by leveraging deeper single-scale linear layers or advanced pooling layers. However, embeddings that focus on local spatial details at a single scale tend to miss the richer spatial context available in earlier layers. This limitation highlights the need for methods that incorporate multi-scale information to enhance the depth and utility of feature descriptors in histopathological image analysis. In this work, we propose the Local-Global Feature Fusion Embedding Model, an approach that consists of a pre-trained backbone for multi-scale feature extraction, a neck branch for local-global feature fusion, and a Generalized Mean (GeM)-based pooling head for generating robust feature descriptors. Our experiments involved training the model’s neck and head on the ImageNet-1k and PanNuke datasets using the Sub-center ArcFace loss function. Performance was evaluated on the Kimia Path24C dataset for histopathological image retrieval. The proposed model achieved a Recall@1 of 99.40% on test patches, outperforming state-of-the-art methods.

Últimamente, el efecto magnetocalórico ha despertado gran interés debido a sus diversas aplicaciones en sistemas de refrigeración. Por esta razón, se han investigado distintos materiales que presenten este fenómeno. En esta tesis se estudia la respuesta térmica del grafeno, tanto analítica como numéricamente, utilizando la estadística de Boltzmann y de Fermi-Dirac, al aplicarle un campo magnético externo variable. Se analizan diversas cantidades termodinámicas en el ensamble canónico, con especial atención a la variación de entropía en una monocapa y una bicapa de grafeno. Los resultados muestran que, según la estadística de Boltzmann, la bicapa tiene una mejor respuesta térmica a baja temperatura, mientras que la monocapa resulta más eficiente al aumentar la temperatura. En cambio, con la estadística de Fermi-Dirac, la bicapa mantiene una mejor respuesta térmica en todo el rango de temperatura analizado. Estos resultados motivan el interés por explorar el comportamiento térmico en estructuras de grafeno con más capas, como una extensión natural de esta investigación.

Organic nitrogen plays a significant role in the fermentation performance and production of esters and higher alcohols. This study assessed the use of yeast protein hydrolysate (YPH) as a nitrogen source for grape must fermentation. In this study, we prepared an enzymatic protein hydrolysate using yeasts recovered from a previous fermentation of wine. Three treatments were performed. DAP supplementation was used as a control, while two YPH treatments were used. Low (LDH) and high degrees of hydrolysis (HDH), 3.5% and 10%, respectively, were chosen. Gas chromatography and principal component analysis indicated a significant positive influence of YPH-supplementations on the production of esters and higher alcohols. Significantly high concentrations of 3-methyl-1-penthanol, isoamyl alcohol, isobutanol, and 2-phenylethanol were observed. Significant odorant activity was obtained for 3-methyl-1-pentanol and ethyl-2-hexenoate. The use of YPH as nitrogen supplementation is justified as a recycling yeasts technique by the increase in volatile compounds.

Electrochemical processes have been developed for a wide range of applications such as, mineral refining, water purification, energy storage and generation. The development of models to describe these processes is very important for their analysis, optimization and operation. The framework of irreversible port-Hamiltonian systems has proven to be an important tool to analyze and integrate thermal models with models of different domains. This work discusses the modeling of non-isothermal electrochemical processes as irreversible port-Hamiltonian systems. An irreversible port-Hamiltonian model based on the internal energy function is derived for a simple but general example. The irreversible model is obtained from the molar and charge balance equations combined with the entropy balance equation. The resulting model can be interpreted as a thermodynamic system and aspects such as entropy production, thermodynamic driving forces and intensive/extensive variables are encoded in the representation. An electrochemical process with two simultaneous reactions is considered to illustrate the approach. The interconnection with a resistive load is also considered to illustrate the benefit of the port-based formulation of the model.

A currently open question is whether the Higgs boson particle is truly elementary (i.e. pointlike), down to distance scales much shorter than the Electroweak scale, or if, on the contrary, it is a composite bound state of more fundamental degrees of freedom, whose physics should be revealed at energies not far above the weak scale. In either case the discovery of this scalar particle was truly remarkable. If it turns out to be elementary, it would be the first and only known example of this kind in nature. On the other hand, if it turns out that the Higgs boson is a composite state arising from some underlying strong dynamics, we would be in a situation that also presents new characteristics compared to other known composite scalars. We elaborate on this last possibility in the present thesis. This thesis aims to be a pedagogical and self-contained theoretical review of some of the most relevant aspects of general composite Higgs theories. It begins with section 1 reviewing the basics of the Standard Model. Section 2 reviews some important characteristic of the Higgs boson: its most important decay and production channels, as well as its radiative corrections. Section 3 explains the hierarchy problem, and presents the best-known approaches that address this problem. In section 4 we explain the composite sector of a composite Higgs theory and proceed to present the CCWZ prescription, a fundamental tool when it comes to understand the low-energy and confinement regime of a strong dynamic. Section 5 present the elementary and composite sectors. In section 6 we introduce extra spin-0 and spin-1 resonances. Finally, in section 7 we introduce the phenomenology of a particular composite Higgs model analyzed in Ref. [123] and in which the author of the thesis was part.