Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset - based on the city of Porto -, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on Intelligent Transportation System

Thermal modeling of industrial-scale vanadium redox flow batteries in high-current operations

A cell-resolved model that simulates the dynamic thermal behavior of a Vanadium Redox Flow Battery during charge and discharge is presented. It takes into account, at a cell level, the reversible entropic heat of the electrochemical reactions, irreversible heat due to overpotentials, self-discharge reactions due to ion crossover, and shunt current losses. The model accounts for the heat transfer between cells and toward the environment, the pump hydraulic losses and the heat transfer of piping and tanks. It provides the electrolyte temperature in each cell, at the stack inlet and outlet, along the piping and in the tanks. Validation has been carried out against the charge/discharge measurements from a 9kW/27kWh VRFB test facility. The model has been applied to study a VRFB with the same stack but a much larger capacity, operating at \uf0b1400 A for 8 h, in order to identify critical thermal conditions which may occur in next-generation industrial VRFB stacks capable to operating at high current density. The most critical condition has been found at the end a long discharge, when temperatures above 50\ub0C appeared, possibly resulting in \u3016VO\u3017_2^+ precipitation and battery faults. These results call for heat exchangers tailored to assist high-power VRFB systems

Multiphysics Finite\u2013Element Modelling of an All\u2013Vanadium Redox Flow Battery for Stationary Energy Storage

All-Vanadium Redox Flow Batteries (VRFBs) are emerging as a novel technology for stationary energy storage. Numerical models are useful for exploring the potential performance of such devices, optimizing the structure and operating condition of cell stacks, and studying its interfacing to the electrical grid. A one-dimensional steady-state multiphysics model of a single VRFB, including mass, charge and momentum transport and conservation, and coupled to a kinetic model for electrochemical reactions, is first presented. This model is then extended, including reservoir equations, in order to simulate the VRFB charge and discharge dynamics. These multiphysics models are discretized by the finite element method in a commercial software package (COMSOL). Numerical results of both static and dynamic 1D models are compared to those from 2D models, with the same parameters, showing good agreement. This motivates the use of reduced models for a more efficient system simulation

L-Phenylalanine Transport in Saccharomyces cerevisiae: Participation of GAP1, BAP2, and AGP1

We focused on the participation of GAP1, BAP2, andAGP1 in L-phenylalanine transport in yeast. In order to study the physiological functions of GAP1, BAP2, and AGP1 in L-phenylalanine transport, we examined the kinetics, substrate specificity, and regulation of these systems, employing isogenic haploid strains with the respective genes disrupted individually and in combination. During thecharacterization of phenylalanine transport, we noted important regulatory phenomena associated with these systems. Our results show that Agp1p is the major transporter of the phenylalanine in a gap1 strain growing in synthetic media with leucine present as an inducer. In a wild type straiFil: Stella, Carlos Alberto. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Bioquímica Humana; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chianelli, Monica Silvia. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Bioquímica Humana; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Saenz, Daniel Alberto. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Bioquímica Humana; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

"Spoon-feeding" an AGN

Tidal disruption events (TDEs) occur when a star, passing too close to a massive black hole, is ripped apart by tidal forces. A less dramatic event occurs if the star orbits just outside the tidal radius, resulting in a mild stripping of mass. Thus, if a star orbits a central black hole on one of these bound eccentric orbits, weaker outbursts will occur recurring every orbital period. Thanks to five Swift observations, we observed a recent flare from the close by (92 Mpc) galaxy IC 3599, where a possible TDE was already observed in December 1990 during the Rosat All-Sky Survey. By light curve modeling and spectral fitting, we account for all these events as the non-disruptive tidal stripping of a single star into a 9.5 yr highly eccentric bound orbit. This is the first example of periodic partial tidal disruptions, possibly spoon-feeding the central black hole.Comment: 7 pages, 3 figures, to appear in "Swift:10 years of discovery", Proceedings of Scienc

Moment-based metrics for global sensitivity analysis of hydrological systems

We propose new metrics to assist global sensitivity analysis, GSA, of hydrological and Earth systems. Our approach allows assessing the impact of uncertain parameters on main features of the probability density function, pdf, of a target model output, y. These include the expected value of y, the spread around the mean and the degree of symmetry and tailedness of the pdf of y. Since reliable assessment of higher order statistical moments can be computationally demanding, we couple our GSA approach with a surrogate model, approximating the full model response at a reduced computational cost. Here, we consider the generalized Polynomial Chaos Expansion (gPCE), other model reduction techniques being fully compatible with our theoretical framework. We demonstrate our approach through three test cases, including an analytical benchmark, a simplified scenario mimicking pumping in a coastal aquifer, and a laboratory-scale conservative transport experiment. Our results allow ascertaining which parameters can impact some moments of the model output pdf while being uninfluential to others. We also investigate the error associated with the evaluation of our sensitivity metrics by replacing the original system model through a gPCE. Our results indicate that the construction of a surrogate model with increasing level of accuracy might be required depending on the statistical moment considered in the GSA. Our approach is fully compatible with (and can assist the development of) analysis techniques employed in the context of reduction of model complexity, model calibration, design of experiment, uncertainty quantification and risk assessment

A Propose-and-revise System for Real-time Traffic Management

The aim of this paper is to describe an intelligent system for the problem of real time road traffic control. The purpose of the system is to help traffic engineers in the selection of the state of traffic control devices on real time, using data recorded by traffic detectors on motorways. The system follows an advanced knowledge-based approach that implements an abstract generic problem solving method, called propose-and-revise, which was proposed in Artificial Intelligence, within the knowledge engineering field, as a standard cognitive structure oriented to solve configuration design problems. The paper presents the knowledge model of such a system together with the strategy of inference and describes how it was applied for the case of the M-40 urban ring for the city of Madrid

Predicción del rendimiento de híbridos de maíz (Zea mays L.) en ambientes de siembra tardía

La predicción precisa del comportamiento de híbridos de maíz no evaluados a campo permitirá un mayor progreso genético y menores costos en programas de mejoramiento genético. Datos de rendimiento de híbridos evaluados a campo se emplearon para predecir el comportamiento de nuevos híbridos en ambientes de siembra tardía. Se conformaron grupos de híbridos predictores de manera de maximizar y minimizar las relaciones de parentesco entre los híbridos predictores y aquellos a predecir y, por otra parte, utilizar valores de predicción obtenidos en ambientes de alto rendimiento y bajo rendimiento a fin de investigar la influencia de estos factores sobre la eficiencia de las predicciones. A fin de validar las predicciones se tomó un grupo de híbridos cuyo rendimiento en grano fue evaluado a campo, pero que no formaron parte del grupo inicial. Se calcularon los coeficientes de correlación entre los valores predichos y los observados para rendimiento con el fin de evaluar la efectividad de la predicción realizada. La mejor predicción de los híbridos no evaluados, se alcanzó utilizando la máxima relación de parentesco entre los híbridos combinada con datos obtenidos en el ambiente de mayor rendimiento promedio.Accurate prediction of the phenotypical performance of untested single-cross hybrids allows for a faster genetic progress of the breeding pool at a reduced cost. Yield data of maize hybrids were employed to predict the performance of new untested hybrids in late sowing environments. Different groups of predictor hybrids were formed using both data from high and low relatedness between predictors and predicted hybrids and by employing data from low and high yielding environments. A new group of hybrids were formed and evaluated in field trials to validate the predictions. The effectiveness of the predictions was investigated by means of the correlation coefficient between predicted and observed yield values. The best predictions of untested new hybrids were reached by using maximum relatedness information combined with data obtained in the best yielding environments.Fil: Biasutti, Carlos Alberto. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; ArgentinaFil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Area de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin