Metodología para el desarrollo de centrales hidroeléctricas

La energía hidroeléctrica tiene suficientes recursos disponibles y es promovida activamente por los gobiernos como parte de su matriz energética, pero su desarrollo se ve limitado por la dificultad de abordar las particularidades de la ubicación, incluidas características técnicas como geología o hidrología, y características institucionales como la aceptación social y las limitaciones ambientales. y el marco regulatorio. Los resultados del proyecto surgen de las interacciones de estas características inherentes y la arquitectura del proyecto y no siempre cumplen con las expectativas de las partes interesadas, lo que lleva a resultados de proyecto deficientes y pérdida de valor.Tesi

Critical formation of trapped surfaces in the collision of gravitational shock waves

We study the formation of marginally trapped surfaces in the head-on collision of two shock waves both in anti-de Sitter and Minkowski space-time in various dimensions as a function of the spread of the energy density in transverse space. For D=4 and D=5 it is shown that there exists a critical value of this spread above which no solution to the trapped surface equation is found. In four dimensions the trapped surface at criticality has a finite size, whereas in five the critical size vanishes. In both cases we find scaling relations characterized by critical exponents. Finally, when D>5 there is always a solution to the trapped surface equation for any transverse spread.Comment: 16 page

Analysis Of The Cyclability Of Lithium-polymer Batteries

Comunicación y póster en congresoLithium ion batteries and similar energy storage devices have an increasing importance for the modern society as they are present in many portable electronic devices and have perspectives in the fields of electric vehicles and renewable energy accumulation. Herein, we present results from charge and discharge cycles on batteries under controlled conditions. The cyclability of commercial lithium-polymer pouch batteries under different charge/discharge rates and temperatures was studied. Based on the results, the relationship between the state of charge and the cell voltage was obtained, as well as degradation of the cells, i.e., the decrease of the energy capacity after a number of cycles. The experimental results were compared with simulations based on Newman's model for Lithium Ion Batteries, carried out using the COMSOL Multiphysics® software. The batteries and fuel cell and the heat transfer modules were use to couple between the temperature and the electrochemical interactions. The results show the correlation between temperature, C-rate and degradation in lithium ion batteries. It is specially remarkable the decrease of the apparent capacity of batteries at low temperatures, and the increase of the degradation at higher temperatures. These results are essential for the design of mechanisms that could prevent battery failure.The authors acknowledge the financial support from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 778045, and the "Plan Propio de Investigación y Transferencia de la Universidad de Málaga", code: PPIT.UMA.B5.2018/17

Leveraging Machine Learning Techniques towards Intelligent Networking Automation

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the computational costs of implementing the proposed mechanisms. Accordingly, this thesis tackles the challenges that four specific research problems present. The first topic addresses the problem of balancing traffic in dense Internet of Things (IoT) network scenarios where the end devices and the Base Stations (BSs) form complex networks. By applying ML techniques to discover patterns in the association between the end devices and the BSs, the proposed scheme can balance the traffic load in a IoT network to increase the packet delivery ratio and reduce the energy cost of data delivery. The second research topic proposes an intelligent congestion control for internet connections at edge network elements. The design includes a congestion predictor based on an Artificial Neural Network (ANN) and an Active Queue Management (AQM) parameter tuner. Similarly, the third research topic includes an intelligent solution to the inter-domain congestion. Different from second topic, this problem considers the preservation of the private network data by means of Federated Learning (FL), since network elements of several organizations participate in the intelligent process. Finally, the fourth research topic refers to a framework to efficiently gathering network telemetry (NT) data. The proposed solution considers a traffic-aware approach so that the NT is intelligently collected and transmitted by the network elements. All the proposed schemes are evaluated through use cases considering standardized networking mechanisms. Therefore, we envision that the solutions of these specific problems encompass a set of methods that can be utilized in real-world scenarios towards the realization of the INA paradigm

Ultra-High Energy Probes of Classicalization

Classicalizing theories are characterized by a rapid growth of the scattering cross section. This growth converts these sort of theories in interesting probes for ultra-high energy experiments even at relatively low luminosity, such as cosmic rays or Plasma Wakefield accelerators. The microscopic reason behind this growth is the production of N-particle states, classicalons, that represent self-sustained lumps of soft Bosons. For spin-2 theories this is the quantum portrait of what in the classical limit are known as black holes. We emphasize the importance of this quantum picture which liberates us from the artifacts of the classical geometric limit and allows to scan a much wider landscape of experimentally-interesting quantum theories. We identify a phenomenologically-viable class of spin-2 theories for which the growth of classicalon production cross section can be as efficient as to compete with QCD cross section already at 100 TeV energy, signaling production of quantum black holes with graviton occupation number of order 10^4.Comment: 23 pages, late

Hymenolepis microstoma (Cestoda: Hymenolepididae) en ratones caseros (Mus musculus) de Lima, Perú

Un total de 12 cestodos adultos se colectaron de los conductos biliares de ratones domésticos (Mus musculus) provenientes de Lima, Perú. Diversas características del escólex y proglotis maduros del cestodo fueron observadas para la identificación morfológica. Así mismo, se realizó un diagnóstico molecular mediante un PCR y secuenciación parcial del gen mitocondrial citocromo c oxidasa subunidad 1 (cox1). Todos los cestodos fueron identificados como Hymenolepis microstoma por morfología y métodos moleculares. El aislado de H. microstoma de Perú mostró una similitud de secuencia significativa (> 99%) con los aislados de H. microstoma previamente reportados. Nuestro informe confirma la presencia del parásito en ratones de Lima

Innovative Families Of Double-Layer Tensegrity Grids: Quastruts and Sixstruts

Double-layer tensegrity grids (DLTG) are spatial reticulated systems based on tensegrity principles, which have been studied in detail over recent years. The most important investigations have been carried out focusing on a short list of tensegrity grids. This paper explains with real examples how to use Rot-Umbela Manipulations, a unique technique developed for generating innovative typologies of tensegrity structures. It is applied to two already existing tensegrity grids in order to obtain two new DLTGs. Their analysis permits us to identify, inside these novel grids, the modules that compose them which were unknown until now. A brief description of these components is provided, as well as some information about their static analysis, e.g. states of self-stress and internal mechanisms. These novel modules belong to a family, all of them with similar characteristics in terms of geometry and topology, and can be used to generate a wide catalogue of DLTGs. Some examples of new grids are presented, describing the methodology on how to obtain many more models for other designers interested in creating and studying innovative DLTGs