From the discrete to the continuous - towards a cylindrically consistent dynamics

Discrete models usually represent approximations to continuum physics. Cylindrical consistency provides a framework in which discretizations mirror exactly the continuum limit. Being a standard tool for the kinematics of loop quantum gravity we propose a coarse graining procedure that aims at constructing a cylindrically consistent dynamics in the form of transition amplitudes and Hamilton's principal functions. The coarse graining procedure, which is motivated by tensor network renormalization methods, provides a systematic approximation scheme towards this end. A crucial role in this coarse graining scheme is played by embedding maps that allow the interpretation of discrete boundary data as continuum configurations. These embedding maps should be selected according to the dynamics of the system, as a choice of embedding maps will determine a truncation of the renormalization flow.Comment: 22 page

Radiotherapy optimAl Design: An Academic Radiotherapy Treatment Design System

Optimally designing radiotherapy and radiosurgery treatments to increase the likelihood of a successful recovery from cancer is an important application of operations research. Researchers have been hindered by the lack of academic software that supports head-to-head comparisons of different techniques, and this article addresses the inherent difficulties of designing and implementing an academic treatment planning system. In particular, this article details the algorithms and the software design of Radiotherapy optimAl Design (RAD)

Higher levels of CO2 during late incubation alter the hatch time of chicken embryos

status: publishe

Extrapolation of Multiplicity distribution in p+p(\bar(p)) collisions to LHC energies

The multiplicity (N_ch) and pseudorapidity distribution (dN_ch/d\eta) of primary charged particles in p+p collisions at Large Hadron Collider (LHC) energies of \sqrt(s) = 10 and 14 TeV are obtained from extrapolation of existing measurements at lower \sqrt(s). These distributions are then compared to calculations from PYTHIA and PHOJET models. The existing \sqrt(s) measurements are unable to distinguish between a logarithmic and power law dependence of the average charged particle multiplicity () on \sqrt(s), and their extrapolation to energies accessible at LHC give very different values. Assuming a reasonably good description of inclusive charged particle multiplicity distributions by Negative Binomial Distributions (NBD) at lower \sqrt(s) to hold for LHC energies, we observe that the logarithmic \sqrt(s) dependence of are favored by the models at midrapidity. The dN_ch/d\eta versus \eta for the existing measurements are found to be reasonably well described by a function with three parameters which accounts for the basic features of the distribution, height at midrapidity, central rapidity plateau and the higher rapidity fall-off. Extrapolation of these parameters as a function of \sqrt(s) is used to predict the pseudorapidity distributions of charged particles at LHC energies. dN_ch/d\eta calculations from PYTHIA and PHOJET models are found to be lower compared to those obtained from the extrapolated dN_ch/d\eta versus \eta distributions for a broad \eta range.Comment: 11 pages and 13 figures. Substantially revised and accepted for publication in Journal of Physics

(Broken) Gauge Symmetries and Constraints in Regge Calculus

We will examine the issue of diffeomorphism symmetry in simplicial models of (quantum) gravity, in particular for Regge calculus. We find that for a solution with curvature there do not exist exact gauge symmetries on the discrete level. Furthermore we derive a canonical formulation that exactly matches the dynamics and hence symmetries of the covariant picture. In this canonical formulation broken symmetries lead to the replacements of constraints by so--called pseudo constraints. These considerations should be taken into account in attempts to connect spin foam models, based on the Regge action, with canonical loop quantum gravity, which aims at implementing proper constraints. We will argue that the long standing problem of finding a consistent constraint algebra for discretized gravity theories is equivalent to the problem of finding an action with exact diffeomorphism symmetries. Finally we will analyze different limits in which the pseudo constraints might turn into proper constraints. This could be helpful to infer alternative discretization schemes in which the symmetries are not broken.Comment: 32 pages, 15 figure

Characterization of Flap Edge Noise Radiation from a High-Fidelity Airframe Model

The results of an experimental study of the noise generated by a baseline high-fidelity airframe model are presented. The test campaign was conducted in the open-jet test section of the NASA Langley 14- by 22-foot Subsonic Tunnel on an 18%-scale, semi-span Gulfstream airframe model incorporating a trailing edge flap and main landing gear. Unsteady surface pressure measurements were obtained from a series of sensors positioned along the two flap edges, and far field acoustic measurements were obtained using a 97-microphone phased array that viewed the pressure side of the airframe. The DAMAS array deconvolution method was employed to determine the locations and strengths of relevant noise sources in the vicinity of the flap edges and the landing gear. A Coherent Output Power (COP) spectral method was used to couple the unsteady surface pressures measured along the flap edges with the phased array output. The results indicate that outboard flap edge noise is dominated by the flap bulb seal cavity with very strong COP coherence over an approximate model-scale frequency range of 1 to 5 kHz observed between the array output and those unsteady pressure sensors nearest the aft end of the cavity. An examination of experimental COP spectra for the inboard flap proved inconclusive, most likely due to a combination of coherence loss caused by decorrelation of acoustic waves propagating through the thick wind tunnel shear layer and contamination of the spectra by tunnel background noise at lower frequencies. Directivity measurements obtained from integration of DAMAS pressure-squared values over defined geometric zones around the model show that the baseline flap and landing gear are only moderately directional as a function of polar emission angle

EEG-EMG-coherence in SDB patients with utilization of a support vector machine-algorithm [Poster Abstract]

Background We investigated whether the EEG-EMG-coherence allows a differentiation between patients with sleep-disordered breathing (SDB) without OSA and SDB-patients with mild, moderate or severe OSA. Methods Polysomnographic recordings of 102 patients with SDB (33 female; age: 53,± 12,4 years) were analyzed with the multitaper coherence method (MTM). Recordings contained 2 EEG-channels (C3 and C4) and a chin EMG-channel for one night. Four epochs (each 30 seconds, classified manually by AASM 2007 criteria) of each sleep stage were marked (1632 epochs in total), which were included in the classification analysis. The collected data sets were supplied to the support vector machine (SVM) algorithm to classify OSA severity. Twenty patients had a mild (RDI ≥10/h and < 15/h), 30 patients had a moderate (RDI ≥15/h and < 30/h) and 27 patients had a severe OSA (RDI ≥30/h). 25 patients had a RDI < 10/h. The AUC (area under the curve) value was calculated for each receiver operator curve (ROC) curve. Results EEG-EMG coherence was able to distinguish between the SDB-patients without OSA and SDB-patients with OSA in each of the 3 severity groups using an SVM algorithm. In mild OSA, the AUC was 0.616 (p = 0.024), in moderate OSA the AUC was 0.659 (p = 0.003), and in severe OSA the AUC was 0.823 (p < 0.001). Conclusions SDB patients with OSA can be differentiated from SDB patients without OSA on the basis of EEG-EMG coherence by using the Multitaper Coherence Method (MTM) and SVM algorithm

Sleep stage classification using spectral analyses and support vector machine algorithm on C3- and C4-EEG signals [Abstract]

Introduction Sleep stage classification currently relies largely on visual classification methods. We tested a new pipeline for automated offline classification based upon power spectrum at six different frequency bands. The pipeline allowed sleep stage classification and provided whole-night visualization of sleep stages. Materials and methods 102 subjects (69 male; 53.74 ± 12.4 years) underwent full-night polysomnography. The recording system included C3- and C4-EEG channels. All signals were measured at sampling rate of 200 Hz. Four epochs (30 seconds each) of each sleep stage (N1, N2, N3, REM, awake) were marked in the visually scored recordings of each one of the 102 patients. Scoring of sleep stages was performed according to AASM 2007-criteria. In total 408 epochs for each sleep stage were included in the sleep stage classification analyses. Recordings of all these epochs were fed into the pipeline to estimate the power spectrum at six different frequency bands, namely from very low frequency (VLF, 0.1-1 Hz) to gamma frequency (30-50 Hz). The power spectrum was measured with a method called multitaper method. In this method the spectrum is estimated by multiplying the data with K windows (i.e tapers).The estimated parameters were given as input to the support vector machine (SVM) algorithm to classify the five different sleep stages based on the mean power amplitude estimated from six different frequency bands. The SVM algorithm was trained with 51 subjects and the testing was done with the other 51 subjects. In order to avoid bias of the training dataset, a 10-fold cross validation was additionally done to check the performance of the SVM algorithm Results The estimated testing accuracy of prediction of the sleep stages was 84.1% for stage N1 using the mean power amplitude from the delta frequency band. Accuracy was 67.8% for stage N2 from the delta frequency band and 74.9% for stage N3 from the VLF. Accuracy was 79.7% for REM stage from the delta frequency band and 84,8% for the wake stage from the theta frequency band. Conclusions We were able to successfully classify the sleep stages using the mean power amplitude at six different frequency bands separately and achieved up to 85% accuracy using the electrophysiological EEG signals. The delta and theta frequency bands gave the best accuracy of classification among all sleep stages