Resultados totales (Incluyendo duplicados): 34669
Encontrada(s) 3467 página(s)
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/140526
Dataset. 2020

[SUPPLEMENTARY MATERIAL] MULTIDIMENSIONAL TAXONOMY OF UNIVERSITY RANKINGS

  • Orduña Malea, Enrique
  • Perez-Esparrells, Carmen
Se incluye una muestra de 60 rankings globales de universidades, categorizados en función de una taxonomía propuesta. Estos contenidos constituyen los datos utilizados en un capítuo de libro de futura publicación.

Proyecto: //
DOI: https://riunet.upv.es/handle/10251/140526
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/140526
HANDLE: https://riunet.upv.es/handle/10251/140526
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/140526
PMID: https://riunet.upv.es/handle/10251/140526
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/140526
Ver en: https://riunet.upv.es/handle/10251/140526
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/140526

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/146502
Dataset. 2020

2019 SPANISH GENERAL ELECTIONS FACEBOOK ADS DATASET

  • Baviera Puig, Tomás
The sponsored content on social networks has raised new questions and research objectives, including discussions regarding the budget invested in these sites by political parties, as well as the new central role that technology corporations play in electoral contexts. This dataset comprises 10,485 Facebook Ad corpus obtained from the Facebook Ad Library through a web crawler developed in Pyhton. The dataset elements correspond to the Facebook Ads paid by the main national political parties during the two 2019 Spanish General Elections precampaign and campaign. The parties are the following: Partido Socialista Obrero Español (PSOE), Partido Popular (PP), Ciudadanos, Unidas Podemos (coalition of Podemos and Izquierda Unida) and Vox. The General Elections were held on April 28 and November 10., We would like to thank Exponentia CIO Juan Besari for providing the appropriate hardware to carry out the Facebook Ad extraction processes.

DOI: https://riunet.upv.es/handle/10251/146502
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/146502
HANDLE: https://riunet.upv.es/handle/10251/146502
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/146502
PMID: https://riunet.upv.es/handle/10251/146502
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/146502
Ver en: https://riunet.upv.es/handle/10251/146502
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/146502

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/148730
Dataset. 2020

INPUT DATA OF A LINEAR PROGRAMMING MODEL FOR THE E-GROCERY ORDER PICKING AND DELIVERY PROBLEM

  • Vazquez-Noguerol, Mar
  • Comesaña Benavides, José Antonio
  • Prado Prado, Jose Carlos
  • Poler Escoto, Raúl
The dataset contains the input data that has been entered into the mathematical model to develop the case study. The data is provided by the company and the values have been structured according to the indices defined in the model. Input data details have been added to make the model replicable.

Proyecto: //
DOI: https://riunet.upv.es/handle/10251/148730
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/148730
HANDLE: https://riunet.upv.es/handle/10251/148730
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/148730
PMID: https://riunet.upv.es/handle/10251/148730
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/148730
Ver en: https://riunet.upv.es/handle/10251/148730
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/148730

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/207104
Dataset. 2022

THE OPTIMAL CONCENTRATION OF NANOCLAY HYDROTALCITE FOR RECOVERY OF REACTIVE AND DIRECT TEXTILE COLORANTS

  • López Rodríguez, Daniel
  • Micó Vicent, Bárbara
  • Bonet Aracil, María Angeles
  • Cases Iborra, Francisco Javier
  • Bou Belda, Eva
Concerns about the health of the planet have grown dramatically, and the dyeing sector of the textile industry is one of the most polluting of all industries. Nanoclays can clean dyeing wastewater using their adsorption capacities. In this study, as a new finding, it was possible to analyze and quantify the amount of metal ions substituted by anionic dyes when adsorbed, and to determine the optimal amount of nanoclay to be used to adsorb all the dye. The tests demonstrated the specific amount of nanoclay that must be used and how to optimize the subsequent processes of separation and processing of the nanoclay. Hydrotalcite was used as the adsorbent material. Direct dyes were used in this research. X-ray diffraction (XRD) patterns allowed the shape recovery of the hydrotalcite to be checked and confirmed the adsorption of the dyes. An FTIR analysis was used to check the presence of characteristic groups of the dyes in the resulting hybrids. The thermo-gravimetric (TGA) tests corroborated the dye adsorption and the thermal fastness improvement. Total solar reflectance (TSR) showed increased radiation protection for UV-VIS-NIR. Through the work carried out, it has been possible to establish the maximum adsorption point of hydrotalcite.

Proyecto: //
DOI: https://riunet.upv.es/handle/10251/207104
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/207104
HANDLE: https://riunet.upv.es/handle/10251/207104
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/207104
PMID: https://riunet.upv.es/handle/10251/207104
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/207104
Ver en: https://riunet.upv.es/handle/10251/207104
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/207104

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/55150
Dataset. 2015

HUMAN ATRIA AND TORSO 3D COMPUTATIONAL MODELS FOR SIMULATION OF ATRIAL ARRHYTHMIAS

MODELOS COMPUTACIONAL 3D DE AURÍCULA Y TORSO HUMANOS REALISTAS PARA LA SIMULACIÓN DE ARRITMIAS AURICULARES

  • Sebastián Aguilar, Rafael
  • Ferrer Albero, Ana
  • Saiz Rodríguez, Francisco Javier
Computational 3D models of human atria and torso with encoded anatomical and functional heterogeneity. The finite element model of the atria is a multi-layer mesh with a homogeneous wall thickness between 600 and 900 μm (754.893 nodes and 515.010 elements), built with linear hexahedral elements and with regular spatial resolution of 300 µm. The model consists of: 21 regions with 53 sub-divisions representing the detailed description of fibre orientation; electrophysiological heterogeneity represented by 8 cellular models with differences in the action potential properties; and conduction velocities and anisotropy ratios tuned to each atrial region. The torso was modelled using a dataset obtained from the online open repository at the Centre for Integrative Biomedical Computing (CBIC) from University of Utah. The torso model has 190.804 nodes and 1.149.531 tetrahedral elements with a spatial resolution of 0.6 mm. It has 6 different regions: lungs, bones, liver, ventricle, blood pools, and flesh.

Proyecto: //
DOI: https://riunet.upv.es/handle/10251/55150
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/55150
HANDLE: https://riunet.upv.es/handle/10251/55150
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/55150
PMID: https://riunet.upv.es/handle/10251/55150
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/55150
Ver en: https://riunet.upv.es/handle/10251/55150
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/55150

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200232
Dataset. 2023

CHARACTERIZATION OF WO3 NANOSTRUCTURES USED IN THE DEGRADATION OF METHYLPARABEN

  • García Antón, José
  • Cifre Herrando, Mireia
  • García García, Dionisio Miguel
  • Roselló Márquez, Gemma
The data were use to study the degradation of Methylparaben using WO3 Nanostructures. WO3 nanostructures were synthesized with different complexing agents (0.05 M H2O2 and 0.1 M citric acid) and annealing conditions (400 _C, 500 _C and 600 _C) to obtain optimal WO3 nanostructures to use them as a photoanode in the photoelectrochemical (PEC) degradation of an endocrine disruptor chemical. X-ray photoelectron spectroscopy was performed to provide information of the electronic states of the nanostructures. The crystallinity of the samples was observed by a confocal Raman laser microscope and X-ray diffraction. Furthermore, photoelectrochemical measurements (photostability, photoelectrochemical impedance spectroscopy, Mott–Schottky and water-splitting test) were also performed using a solar simulator with AM 1.5 conditions at 100 mW_cm-2. Once the optimal nanostructure was obtained, the PEC degradation of methylparaben was carried out. It was followed by ultra-high-performance liquid chromatography and mass spectrometry, which allowed to obtain the concentration of the contaminant during degradation and the identification of degradation intermediates.

DOI: https://riunet.upv.es/handle/10251/200232
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200232
HANDLE: https://riunet.upv.es/handle/10251/200232
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200232
PMID: https://riunet.upv.es/handle/10251/200232
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200232
Ver en: https://riunet.upv.es/handle/10251/200232
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/200232

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202149
Dataset. 2020

OVS-SCM MODULATION (RAW DATA)

  • Pérez Pascual, Mª Asunción
This dataset contains the raw data of the measurements presented in the paper: "A Computational Efficient Nyquist Shaping Approach for Short-Reach Optical Communications" (https://doi.org/10.1109/JLT.2019.2961506). - Medidas_BER_EVM.xls: contains BER and EVM measurements made for different types of modulations for both the HC-SCM modulator and the OVS-SCM, varying the following parameters: oversampling factor, transmitter filter span, rolloff factor, receiver filter span, receiver equalizer order, pre-emphasis filter (in transmission) - Medidas_Potencia.xls: contains BER and EVM values varying the received power for HC-SCM and OVS-SCM modulations; for all QAM modulations under study.

DOI: https://riunet.upv.es/handle/10251/202149
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202149
HANDLE: https://riunet.upv.es/handle/10251/202149
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202149
PMID: https://riunet.upv.es/handle/10251/202149
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202149
Ver en: https://riunet.upv.es/handle/10251/202149
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202149

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
Dataset. 2022

GROW-GREEN CORE KPIS

GROW-GREEN PILOTS MONITORING

  • Orozco Messana, Javier
  • Calabuig Moreno, Raimon
  • Vallés Planells, María Concepción
  • Galiana Galán, Francisco
  • Alfonso Solar, David
  • Peñalvo López, Elisa
  • Andrés Doménech, Ignacio
  • Tudorie, Carla Ana-Maria
[EN] The H2020 project “Green Cities for Climate and Water Resilience, Sustainable Economic Growth, Healthy Citizens and Environments" (GROW GREEN, Grant Agreement: 730283), developed green infrastructure pilots in: Manchester, Valencia and Wroclaw. The monitoring framework supported the pilot analysis and its impact assessment through the development of core Key Performance Indicators (KPIs) through all pilots. The historical evolution of these core KPIs are available on the Grow-Green Open Data platform sharing the software architecture for the smart city platform of Valencia City. It is an implementation of Telefónica’s Thinking Cities platform, which is based on the FIWARE standards and interfaces. All monitoring data are included on this dataset grouped on the core KPIs structure., This research was co-funded by the European Commission through the H2020 project “Green Cities for Climate and Water Resilience, Sustainable Economic Growth, Healthy Citizens and Environments (GROW GREEN)” Grant Agreement: 730283.

DOI: https://riunet.upv.es/handle/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
HANDLE: https://riunet.upv.es/handle/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
PMID: https://riunet.upv.es/handle/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866
Ver en: https://riunet.upv.es/handle/10251/191866
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/191866

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202500
Dataset. 2024

IMAGE DATABASE FOR THE SCIENTIFIC PAPER: DEEP LEARNING ALGORITHM, BASED ON CONVOLUTIONAL NEURAL NETWORKS, FOR EQUIVALENT ELECTRICAL CIRCUIT RECOMMENDATION FOR ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY

  • Pérez Herranz, Valentín
  • Giner Sanz, Juan José
  • Sáez Pardo, Fermín
The present dataset is the database and image database used to train and test the Convolutional Neural Network models of the scientific paper: Deep Learning algorithm, based on convolutional neural networks, for electrical equivalent electrical circuit recommendation for Electrochemical Impedance Spectroscopy

DOI: https://riunet.upv.es/handle/10251/202500
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202500
HANDLE: https://riunet.upv.es/handle/10251/202500
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202500
PMID: https://riunet.upv.es/handle/10251/202500
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202500
Ver en: https://riunet.upv.es/handle/10251/202500
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/202500

RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/162283
Dataset. 2021

INFORMATION ON ENERGY EFFICIENCY BY AGE OF CONSTRUCTION OF THE HOUSE

  • Coll Aliaga, Peregrina Eloína
[EN] Buildings have become a key source of greenhouse gas (GHG) emissions due to the consumption of primary energy, especially when used to achieve thermal comfort conditions. In addition, buildings play a key role for adapting societies to climate change by achieving more energy efficiency. Therefore, buildings have become a key sector to tackle climate change at the local level. However, public decision-makers do not have tools with enough spatial resolution to prioritise and focus the available resources and efforts in an efficient manner. The objective of the research is to develop an innovative methodology based on a geographic information system (GIS) for mapping primary energy consumption and GHG emissions in buildings in cities according to energy efficiency certificates

Proyecto: //
DOI: https://riunet.upv.es/handle/10251/162283
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/162283
HANDLE: https://riunet.upv.es/handle/10251/162283
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/162283
PMID: https://riunet.upv.es/handle/10251/162283
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/162283
Ver en: https://riunet.upv.es/handle/10251/162283
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
oai:riunet.upv.es:10251/162283

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