Polytechnic University of Valencia

About: Polytechnic University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Catalysis & Population. The organization has 16282 authors who have published 40162 publications receiving 850234 citations.

IST-LASAGNE: towards all-optical label swapping employing optical logic gates and optical flip-flops

Abstract: The Information Society Technologies-all-optical LAbel SwApping employing optical logic Gates in NEtwork nodes (IST-LASAGNE) project aims at designing and implementing the first, modular, scalable, and truly all-optical photonic router capable of operating at 40 Gb/s. The results of the first project year are presented in this paper, with emphasis on the implementation of network node functionalities employing optical logic gates and optical flip-flops, as well as the definition of the network architecture and migration scenarios.

Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua)

Abstract: The daily average PM2.5 concentration forecast is a leading component nowadays in air quality research, which is necessary to perform in order to assess the impact of air on the health and welfare of every living being. The present work is aimed at analyzing and benchmarking a neural-network approach to the prediction of average PM2.5 concentrations. The model thus obtained will be indispensable, as a control tool, for the purpose of preventing dangerous situations that may arise. To this end we have obtained data and measurements based on samples taken during the early hours of the day. Results from three different topologies of neural networks were compared so as to identify their potential uses, or rather, their strengths and weaknesses: Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Square Multilayer Perceptron (SMLP). Moreover, two classical models were built (a persistence model and a linear regression), so as to compare their results with the ones provided by the neural network models. The results clearly demonstrated that the neural approach not only outperformed the classical models but also showed fairly similar values among different topologies. Moreover, a differential behavior in terms of stability and length of the training phase emerged during testing as well. The RBF shows up to be the network with the shortest training times, combined with a greater stability during the prediction stage, thus characterizing this topology as an ideal solution for its use in environmental applications instead of the widely used and less effective MLP.

A performance comparison of energy consumption for Mobile Ad Hoc Network routing protocols

Abstract: The design of efficient routing protocols is a fundamental problem in a Mobile Ad-Hoc Network (MANET). Many different protocols have been proposed in the literature, each one based on different characteristics and properties. Some of these protocols have been studied and their performances have been evaluated in detail, focusing on aspects such as routing overhead latency and route length. We concentrate on the energy consumption issues of routing protocols. We present a performance comparison of the DSR, AODV, TORA and DSDV routing protocols with respect to energy consumption, evaluating how the different approaches and algorithms affect the energy usage in mobile devices.

Toward the Development of Ionically Controlled Nanoscopic Molecular Gates

Abstract: An ionically controlled nanoscopic molecular gate has been developed by using functionalized mesoporous materials. The system shows that control of mass transport at nanometric scale can be achieved by using suitable rigid solids and pH-active molecules. The design principle suggests new perspectives in the search of ionically tuned tailored materials and devices with a fine control of mass transport for new applications in fields such as drug delivery, selective removal of toxic species, sensing, or catalysis.

A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs

Abstract: This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative.