Phase transition in the scalar noise model of collective motion in three dimensions

We consider disorder-order phase transitions in the three-dimensional version of the scalar noise model (SNM) of flocking. Our results are analogous to those found for the two-dimensional case. For small velocity (v <= 0.1) a continuous, second-order phase transition is observable, with the diffusion of nearby particles being isotropic. By increasing the particle velocities the phase transition changes to first order, and the diffusion becomes anisotropic. The first-order transition in the latter case is probably caused by the interplay between anisotropic diffusion and periodic boundary conditions, leading to a boundary condition dependent symmetry breaking of the solutions.Comment: 7 pages, 6 figures; submitted to EPJ on 17 of April, 200

Low-cost photoplethysmograph solutions using the Raspberry Pi

Photoplethysmography is a prevalent, non-invasive heart monitoring method. In this paper an implementation of photoplethysmography on the Raspberry Pi is presented. Two modulation techniques are discussed, which make possible to measure these signals by the Raspberry Pi, using an external sound card as A/D converter. Furthermore, it is shown, how can digital signal processing improve signal quality. The presented methods can be used in low-cost cardiac function monitoring, in telemedicine applications and in education as well, since cheap and current hardware are used. Full documentation and open-source software for the measurement available: http://www.noise.inf.u-szeged.hu/Instruments/raspberryplet/Comment: 14th IEEE International Symposium on Computational Intelligence and Informatics (CINTI 2013), November 19-21, 2013, Budapest, Hungar

GEFCOM 2014 - Probabilistic Electricity Price Forecasting

Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from the experiment reveal that a multi-model approach is significantly better performing in terms of error metrics. Gradient Boosting can deal with seasonality and auto-correlation out-of-the box and achieve lower rate of normalized mean absolute error on real-world data.Comment: 10 pages, 5 figures, KES-IDT 2015 conference. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19857-6_

Macrophage PPARg , a Lipid Activated Transcription Factor Controls the Growth Factor GDF3 and Skeletal Muscle Regeneration

Tissue regenerationrequiresinflammatoryand repar- atory activity of macrophages. Macrophages detect and eliminate the damaged tissue and subsequently promote regeneration. This dichotomy requires the switch of effector functions of macrophages coordi- nated with other cell types inside the injured tissue.\ud The gene regulatory events supporting the sensory and effector functions of macrophages involved in tissue repair are not well understood. Here we show that the lipid activated transcription factor, PPAR g , is required for proper skeletal muscle regeneration, acting in repair macrophages. PPAR g controls the expression of the transforming growth factor- b (TGF- b ) family member, GDF3, which in turn regu- lates the restoration of skeletal muscle integrity by promoting muscle progenitor cell fusion. This work establishes PPAR g as a required metabolic sensor and transcriptional regulator of repair macrophages. Moreover, this work also establishes GDF3 as a secreted extrinsic effector protein acting on myo- blasts and serving as an exclusively macrophage- derived regeneration factor in tissue repair