El valor agregado de las redes sociales: propuesta metodológica para el análisis del capital social

En el presente trabajo se utiliza una variable reticular para estratificar un grupo de actores sociales, lo cual permite exhibir la posición estructural de sus integrantes en base al origen de su capital social. Se utiliza la variable constricción debido a que integra en una sola medida las dos fuentes principales de capital social: cierre e intermediación. Este enfoque, permite generar hipótesis inmediatas sobre los mecanismos subyacentes a la Posición estructural que actualmente posee cada individuo; sobre las razones por las cuales unos individuos son más exitosos que otros (recursos sociales) y, sobre las acciones posibles para generar efectos agregados que generen valor en la red general

Secuencias inferopaleolíticas en la cuenca media del Duero

Síntesis de posibles secuencias de terrazas fluviales pleistocénicas, en la cuenca del Duero, con industrias líticas datables en el Paleolítico Inferior.Synthesis of the presumed sequences of pleistocaenic fluvial terraces in theDuero basin (Spain) with lithic industries dated from lower Paleolithic

Influence of the Pump Control System in the Selection of the Number of Fixed Speed and Variable Speed Drive Pumps in Water Pumping Stations

[EN] Proper design of a pumping system requires the use of a pump curve and set-point curve system. Both have to be as close as possible to optimize energy use. This is achieved by control systems in which the type of control (flow or pressure) and the combination between fixed speed drive (FSD) pumps and variable speed drive (VSD) pumps are involved. The objective of this work is to determine the optimal number of FSD and VSD pumps for each flow rate range in order to discuss the classic design of pumping stations and their control systems. For this, a methodology is applied that defines the parametric form of the pump curve, efficiency curve, and set-point curve in relation to the most efficient point. In this way, dimensionless expressions are obtained and the influence of the set-point parameters on the design of the control system can be analyzed. Additionally, the method includes an expression that estimates the performance of the frequency inverter, which is based on the load and pump speed rotation. The application of the methodology to different case studies allows us to question many classic procedures for pumping stations. In summary, it can be concluded that the appropriate number of variable speed pumps for each control system cannot be established in advance but requires an in-depth study of different available options.Briceño, C.; Iglesias Rey, PL.; Martínez-Solano, FJ. (2019). Influence of the Pump Control System in the Selection of the Number of Fixed Speed and Variable Speed Drive Pumps in Water Pumping Stations. Proceedings. 48(1):1-11. https://doi.org/10.3390/ECWS-4-06445S11148

Do DL models and training environments have an impact on energy consumption?

Current research in the computer vision field mainly focuses on improving Deep Learning (DL) correctness and inference time performance. However, there is still little work on the huge carbon footprint that has training DL models. This study aims to analyze the impact of the model architecture and training environment when training greener computer vision models. We divide this goal into two research questions. First, we analyze the effects of model architecture on achieving greener models while keeping correctness at optimal levels. Second, we study the influence of the training environment on producing greener models. To investigate these relationships, we collect multiple metrics related to energy efficiency and model correctness during the models' training. Then, we outline the trade-offs between the measured energy efficiency and the models' correctness regarding model architecture, and their relationship with the training environment. We conduct this research in the context of a computer vision system for image classification. In conclusion, we show that selecting the proper model architecture and training environment can reduce energy consumption dramatically (up to 98.83%) at the cost of negligible decreases in correctness. Also, we find evidence that GPUs should scale with the models' computational complexity for better energy efficiency.Comment: 49th Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA). 8 pages, 3 figure

Multi-Objective Optimization of Drainage Networks for Flood Control in Urban Area Due to Climate Change

[EN] The Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) of the United Nations mentions that extreme rainfalls might increase their intensity and frequency in most mid-latitude locations and tropical regions by the end of this century, as a consequence of the rise of the average global surface temperature. Human action has given way to global warming which manifests with an increase in extreme rainfall. If these climatic conditions are added to the waterproofing that cities have been experiencing as a result of urban development, a scenario of growing concern for the managers of drainage systems is generated. The objective of drainage networks is preventing the accumulation of rainwater on the surface. Under the new conditions of climate change, these need to be modified and adapted to provide cities with the security they demand. The following article describes a method for flood control by using a rehabilitation model that connects the Storm Water Management Model (SWMM) 5 model with a genetic algorithm to find the best solutions to the flood problem. The final analysis is performed using the Pareto efficiency criteria. The innovation of this method is the inclusion of a local head loss in the drainage network, allowing the upstream flow to be retained by decreasing the downstream concentration time. These elements called hydraulic controls improve system performance and are installed in the initial part of some pipes coming out of storm tanks. As a case study, the developed method has been applied in a section of the drainage network of the city of Bogotá.Bayas-Jiménez, L.; Iglesias Rey, PL.; Martínez-Solano, FJ. (2019). Multi-Objective Optimization of Drainage Networks for Flood Control in Urban Area Due to Climate Change. Proceedings. 48(1):1-9. https://doi.org/10.3390/ECWS-4-06451S1948

Search Space Reduction for Genetic Algorithms Applied to Drainage Network Optimization Problems

[EN] In recent years, a significant increase in the number of extreme rains around the world has been observed, which has caused an overpressure of urban drainage networks. The lack of capacity to evacuate this excess water generates the need to rehabilitate drainage systems. There are different rehabilitation methodologies that have proven their validity; one of the most used is the heuristic approach. Within this approach, the use of genetic algorithms has stood out for its robustness and effectiveness. However, the problem to be overcome by this approach is the large space of solutions that algorithms must explore, affecting their efficiency. This work presents a method of search space reduction applied to the rehabilitation of drainage networks. The method is based on reducing the initially large search space to a smaller one that contains the optimal solution. Through iterative processes, the search space is gradually reduced to define the final region. The rehabilitation methodology contemplates the optimization of networks using the joint work of the installation of storm tanks, replacement of pipes, and implementation of hydraulic control elements. The optimization model presented uses a pseudo genetic algorithm connected to the SWMM model through a toolkit. Optimization problems consider a large number of decision variables, and could require a huge computational effort. For this reason, this work focuses on identifying the most promising region of the search space to contain the optimal solution and to improve the efficiency of the process. Finally, this method is applied in real networks to show its validity.This work was supported by the Program Fondecyt Regular (Project No. 1210410 and Project No. 1180660) of the Agencia Nacional de Investigación y Desarrollo (ANID), Chile.Bayas-Jiménez, L.; Martínez-Solano, FJ.; Iglesias Rey, PL.; Mora-Meliá, D. (2021). Search Space Reduction for Genetic Algorithms Applied to Drainage Network Optimization Problems. Water. 13(15):1-24. https://doi.org/10.3390/w13152008S124131

Combining Skeletonization, Setpoint Curves, and Heuristic Algorithms to Define District Metering Areas in the Battle of Water Networks District Metering Areas

[EN] The problem presented in this edition of the Battle of the Water Networks is to define district metering areas (DMAs) in a large network. The problem is addressed in two phases. First, the complexity of the network is simplified by dividing it into three operational areas. Second, an optimization algorithm defines DMAs, looking for the best feasible solution. A preliminary simulation of the network is made. From this, engineering judgment allows for defining an initial set of elements suitable to change. In the second stage, a heuristic algorithm is used to search for the best DMA definition by selecting the locations and settings of the pressure-reducing valves and isolation valves. The network is then divided into two categories: the main pipes and the distribution pipes. Only the distribution pipes can be closed. With these restrictions and those described in the problem, the algorithm looks for the best DMA definition based on both the pressure and demand distribution among all the DMAs.This work was supported by the Program Fondecyt Regular (Project 1180660) of the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), Chile.Martínez-Solano, FJ.; Iglesias Rey, PL.; Mora Melia, D.; Ribelles-Aguilar, J. (2018). Combining Skeletonization, Setpoint Curves, and Heuristic Algorithms to Define District Metering Areas in the Battle of Water Networks District Metering Areas. Journal of Water Resources Planning and Management. 144(6):04018023-1-04018023-7. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000938S04018023-104018023-7144