A Game-theory Analysis of Charging Stations Selection by EV Drivers

We address the problem of Electric Vehicle (EV) drivers' assistance through Intelligent Transportation System (ITS). Drivers of EVs that are low in battery may ask a navigation service for advice on which charging station to use and which route to take. A rational driver will follow the received advice, provided there is no better choice i.e., in game-theory terms, if such advice corresponds to a Nash-equilibrium strategy. Thus, we model the problem as a game: first we propose a congestion game, then a game with congestion-averse utilities, both admitting at least one pure-strategy Nash equilibrium. The former represents a practical scenario with a high level of realism, although at a high computational price. The latter neglects some features of the real-world scenario but it exhibits very low complexity, and is shown to provide results that, on average, differ by 16% from those obtained with the former approach. Furthermore, when drivers value the trip time most, the average per-EV performance yielded by the Nash equilibria and the one attained by solving a centralized optimization problem that minimizes the EV trip time differ by 15% at most. This is an important result, as minimizing this quantity implies reduced road traffic congestion and energy consumption, as well as higher user satisfaction

Genome-wide activity of unliganded estrogen receptor-\u3b1\ua0 in breast cancer cells

Estrogen receptor-\u3b1 (ER\u3b1) has central role in hormone-dependent breast cancer and its ligand-induced functions have been extensively characterized. However, evidence exists that ER\u3b1 has functions that are independent of ligands. In the present work, we investigated the binding of ER\u3b1 to chromatin in the absence of ligands and its functions on gene regulation. We demonstrated that in MCF7 breast cancer cells unliganded ER\u3b1 binds to more than 4,000 chromatin sites. Unexpectedly, although almost entirely comprised in the larger group of estrogen-induced binding sites, we found that unliganded-ER\u3b1 binding is specifically linked to genes with developmental functions, compared with estrogen-induced binding. Moreover, we found that siRNA-mediated down-regulation of ER\u3b1 in absence of estrogen is accompanied by changes in the expression levels of hundreds of coding and noncoding RNAs. Down-regulatedmRNAs showed enrichment in genes related to epithelial cell growth and development. Stable ER\u3b1 down-regulation using shRNA, which caused cell growth arrest, was accompanied by increased H3K27me3 at ER\u3b1 binding sites. Finally, we found that FOXA1 and AP2\u3b3 binding to several sites is decreased upon ER\u3b1 silencing, suggesting that unliganded ER\u3b1 participates, together with other factors, in the maintenance of the luminal-specific cistrome in breast cancer cell

Towards a Realistic Optimization of Urban Traffic Flows

In spite of recent advances in Intelligent Transport, vehicular traffic dynamics are still hard to represent and analyze. Most of the previous work on traffic regards highways or single lanes where vehicles interact in one dimension. Models for multi-dimensional vehicle-to-vehicle interactions and models for urban intersections are quite complicated and hardly applicable on a large scale. Nonetheless, urban traffic jams are an actual problem that requires a solution. This paper proposes a method to optimize urban traffic layout using basic heuristics and computationally efficient simulations. Instead of modeling an entire urban map with hundreds of intersections, each typology of intersection is simulated in order to understand how it responds to different traffic patterns and intensities. This knowledge is leveraged to allow the computation of minimal delay route on the complete road map. In order to validate our model, we use the solution obtained with our heuristic to derive the average travel delay through simulation on realistic Manhattan topologies with different intersection types. \uc2\ua9 2012 IEEE

First passage time problems and related computational methods

Motivated by the interest of first passage time problems in neurobiology and in a variety of applied fields, we study the solution of first passage time equations for the Wiener and the Ornstein Uhlenbeck process both in the general case of time dependent threshold functions. Some computational results obtained by two different numerical methods are reported and briefly discussed