A new, efficient algorithm for the Forest Fire Model

The Drossel-Schwabl Forest Fire Model is one of the best studied models of non-conservative self-organised criticality. However, using a new algorithm, which allows us to study the model on large statistical and spatial scales, it has been shown to lack simple scaling. We thereby show that the considered model is not critical. This paper presents the algorithm and its parallel implementation in detail, together with large scale numerical results for several observables. The algorithm can easily be adapted to related problems such as percolation.Comment: 38 pages, 28 figures, REVTeX 4, RMP style; V2 is for clarifications as well as corrections and update of reference

Avalanche Behavior in an Absorbing State Oslo Model

Self-organized criticality can be translated into the language of absorbing state phase transitions. Most models for which this analogy is established have been investigated for their absorbing state characteristics. In this article, we transform the self-organized critical Oslo model into an absorbing state Oslo model and analyze the avalanche behavior. We find that the resulting gap exponent, D, is consistent with its value in the self-organized critical model. For the avalanche size exponent, \tau, an analysis of the effect of the external drive and the boundary conditions is required.Comment: 4 pages, 2 figures, REVTeX 4, submitted to PRE Brief Reports; added reference and some extra information in V

Self-organized Criticality and Absorbing States: Lessons from the Ising Model

We investigate a suggested path to self-organized criticality. Originally, this path was devised to "generate criticality" in systems displaying an absorbing-state phase transition, but closer examination of the mechanism reveals that it can be used for any continuous phase transition. We used the Ising model as well as the Manna model to demonstrate how the finite-size scaling exponents depend on the tuning of driving and dissipation rates with system size.Our findings limit the explanatory power of the mechanism to non-universal critical behavior.Comment: 5 pages, 2 figures, REVTeX

Validity of asthma diagnoses in the Danish National Registry of Patients, including an assessment of impact of misclassification on risk estimates in an actual dataset

Annette Østergaard Jensen1, Gunnar Lauge Nielsen2, Vera Ehrenstein11Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; 2Department of Medicine, Himmerland Hospital, Farsø, DenmarkObjective: Asthma diagnoses recorded in the Danish National Registry of Patients (DNRP) are a misclassified measure of the actual asthma status. We quantified this misclassification and examined its impact on the results of an epidemiologic study on asthma.Study design and setting: We validated the DNRP asthma diagnoses against records of asthma diagnosed at medical examinations conducted during mandatory conscription evaluation. We had data on 22,177 male conscripts who were born from January 1st, 1977 to December 31st, 1983, in a conscription district in northern Denmark. We obtained asthma diagnoses recorded among the conscripts in the DNRP from January 1st, 1977 through December 31st, 2003. We estimated sensitivity, specificity, and positive predictive value (PPV) of the DNRP asthma diagnoses. We then conducted sensitivity analysis to quantify the impact of nondifferential misclassification on the rate ratios measuring the association between asthma and risks of different skin cancers.Results: The sensitivity of the DNRP for detecting an asthma diagnosis was 0.44 (95% confidence interval [CI]: 0.42–0.47), the specificity was 0.98 (95% CI: 0.98–0.99) and the PPV was 0.65 (95% CI: 0.62–0.68). Both direct and inverse associations between asthma and the different types of skin cancers became more pronounced after correcting for the misclassification.Conclusion: The DNRP registered asthma diagnosis may be used to measure asthma status in epidemiologic studies seeking to estimate relative effects of asthma. Even at low values of DNRP sensitivity of asthma diagnoses were not sufficient to nullify observed relative associations in an actual dataset. The specificity of DNRP asthma diagnosis is high.Keywords: asthma, validity, registry data, epidemiolog

Use of Machine Learning for Non-Invasive Identification of Tumors

Under Utah Valley University (UVU) Physics Department the Center for Imaging and Biophotonic Experiments Advancing Medicine. (CIBEAM) is focused on techniques for early cancer detection. The undergraduate based research project with the instruction of Dr. Vern Hart has developed a methodology using the scattering profile of a laser through a specimen to classify between different cancer cell types with machine learning algorithms. This technique works by passing a near-infrared (NIR) laser through a cell monolayer and collecting the scattering profile. Thousands of images are collected for the purpose of training a artificially intelligent convolution neural network as well as to build a testing dataset for verification. Additionally, this group has focused on involving many students in various research thanks to the many facets required for this project such as lasers, fabrication, 3D printing, cell growth, and machine learning

Effect of high intensity activity on children's aerobic power

Several studies in recent years have reported significant fitness improvements in children after exercise interventions, given sufficient training intensity, frequency and duration. However the knowledge of how fast children respond to exercise is limited and most research protocols are performed on cycle ergometers or other training devices using exercise programs are not applicable for practical purposes such as children’s sports programs or physical education classes. The aim of this study was to investigate the effect of high intensity aerobic activity in five weeks, two sessions per week, on children’s VO2peak. Additionally we wanted to test whether ten-year old boys were able to sustain intensity over 80 % of reart ratepeak (HRpeak) for a relative long period of time (up to 4 minutes), using aerobic games and play. Results from the present study showed a significant increase in VO2peak from 178.5 (± 12.6) ml x kg-0.67 x min-1 to188.4 (± 12.6) ml x kg-0.67 x min-1). Conclusion: Children can increase their VO2peak after five weeks of exercise with two high intensity sessions per week. This study also revealed that children can exercise near their HRpeak in time periods lasting up to four minutes