Comparing Wireless Traffic Tracking with Regular Traffic Control Systems for the Detection of Congestions in Streets

Detecting congestions on streets is one of the main issues in the area of smart cities. Regular monitoring methods can supply information about the number of vehicles in transit and thus the saturation of the streets, but they are usually expensive and intrusive with respect to the road. In recent years a new trend in traffic detection has arisen, considering the Wireless signals emitted by ‘smart’ on-board devices for counting and tracking vehicles. In this paper, two traffic monitoring methods are compared: detections using a regular Inductive Loop Detector on the road and an own Wireless Tracking System based on Bluetooth detection called Mobywit. The correlation between the day of the week and the hour with the traffic flow in a metropolitan busy street has been analysed. Assuming that our system is not able to defect all the vehicles, but just only subset of them, it is expected a causality between the results obtained using the two methods. This means, that the Bluetooth-based system can detect the same variations in the traffic flow that the regular loop detector, but having two main advantages: the tracking possibilities and a much lower cost.This work has been supported in part by project MOSOS (reference PRY142/14), which has been granted by Fundación Pública Andaluza Centro de Estudios An- daluces in the call `IX Convocatoria de Proyectos de Investigación'. It also has been partially funded by national projects TIN2014-56494-C4-3-P and TEC2015- 68752 (Spanish Ministry of Economy and Competitiveness), PROY-PP2015-06 (Plan Propio 2015 UGR), and project CEI2015-MP-V17 of the Microprojects program 2015 from CEI BioTIC Granada

Effects of university affiliation and “school spirit” on color preferences: Berkeley versus Stanford

The ecological valence theory (EVT) posits that preference for a color is determined by people’s average affective response to everything associated with it (Palmer & Schloss, Proceedings of the National Academy of Sciences, 107, 8877–8882, 2010). The EVT thus implies the existence of sociocultural effects: Color preference should increase with positive feelings (or decrease with negative feelings) toward an institution strongly associated with a color. We tested this prediction by measuring undergraduates’ color preferences at two rival universities, Berkeley and Stanford, to determine whether students liked their university’s colors better than their rivals did. Students not only preferred their own colors more than their rivals did, but the degree of their preference increased with self-rated positive affect (“school spirit”) for their university. These results support the EVT’s claim that color preference is caused by learned affective responses to associated objects and institutions, because it is unlikely that students choose their university or develop their degree of school spirit on the basis of preexisting color preferences

Minding impacting events in a model of stochastic variance

We introduce a generalisation of the well-known ARCH process, widely used for generating uncorrelated stochastic time series with long-term non-Gaussian distributions and long-lasting correlations in the (instantaneous) standard deviation exhibiting a clustering profile. Specifically, inspired by the fact that in a variety of systems impacting events are hardly forgot, we split the process into two different regimes: a first one for regular periods where the average volatility of the fluctuations within a certain period of time is below a certain threshold and another one when the local standard deviation outnumbers it. In the former situation we use standard rules for heteroscedastic processes whereas in the latter case the system starts recalling past values that surpassed the threshold. Our results show that for appropriate parameter values the model is able to provide fat tailed probability density functions and strong persistence of the instantaneous variance characterised by large values of the Hurst exponent is greater than 0.8, which are ubiquitous features in complex systems.Comment: 18 pages, 5 figures, 1 table. To published in PLoS on

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Spatio-temporal diffusion of residential land prices across Taipei regions

ABSTRACT: Past studies have shown that changes in the house price of a region may transmit to its neighbouring regions. The transmission mechanism may follow spatial and temporal diffusion processes. This paper investigates such regional housing market dynamics and interactions among local housing sub-markets in Taipei. The analysis is based on a panel data framework and spatial panel models using annual data on median residential land prices from 41 Taipei sub-markets over the period from 1992 to 2010. The empirical analysis suggests that spatial dependence plays a significant role in interactions among regional housing markets. The results are strongly robust across several model specifications and regions controlling for time fixed effects and space-time covariance. These findings have significant implications for urban spatial planning and efficient use of public resources in mega-urban areas. JEL CLASSIFICATIONS: C21; C23; R12; H5

Histone deacetylase adaptation in single ventricle heart disease and a young animal model of right ventricular hypertrophy.

BackgroundHistone deacetylase (HDAC) inhibitors are promising therapeutics for various forms of cardiac diseases. The purpose of this study was to assess cardiac HDAC catalytic activity and expression in children with single ventricle (SV) heart disease of right ventricular morphology, as well as in a rodent model of right ventricular hypertrophy (RVH).MethodsHomogenates of right ventricle (RV) explants from non-failing controls and children born with a SV were assayed for HDAC catalytic activity and HDAC isoform expression. Postnatal 1-day-old rat pups were placed in hypoxic conditions, and echocardiographic analysis, gene expression, HDAC catalytic activity, and isoform expression studies of the RV were performed.ResultsClass I, IIa, and IIb HDAC catalytic activity and protein expression were elevated in the hearts of children born with a SV. Hypoxic neonatal rats demonstrated RVH, abnormal gene expression, elevated class I and class IIb HDAC catalytic activity, and protein expression in the RV compared with those in the control.ConclusionsThese data suggest that myocardial HDAC adaptations occur in the SV heart and could represent a novel therapeutic target. Although further characterization of the hypoxic neonatal rat is needed, this animal model may be suitable for preclinical investigations of pediatric RV disease and could serve as a useful model for future mechanistic studies

Modeling growth, lipid accumulation and lipid turnover in submerged batch cultures of Umbelopsis isabellina

The production of lipids by oleaginous yeast and fungi becomes more important because these lipids can be used for biodiesel production. To understand the process of lipid production better, we developed a model for growth, lipid production and lipid turnover in submerged batch fermentation. This model describes three subsequent phases: exponential growth when both a C-source and an N-source are available, carbohydrate and lipid production when the N-source is exhausted and turnover of accumulated lipids when the C-source is exhausted. The model was validated with submerged batch cultures of the fungus Umbelopsis isabellina (formerly known as Mortierella isabellina) with two different initial C/N-ratios. Comparison with chemostat cultures with the same strain showed a significant difference in lipid production: in batch cultures, the initial specific lipid production rate was almost four times higher than in chemostat cultures but it decreased exponentially in time, while the maximum specific lipid production rate in chemostat cultures was independent of residence time. This indicates that different mechanisms for lipid production are active in batch and chemostat cultures. The model could also describe data for submerged batch cultures from literature well

Transfer entropy—a model-free measure of effective connectivity for the neurosciences

Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain’s activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction