Renormalization group theory for percolation in time-varying networks

Motivated by multi-hop communication in unreliable wireless networks, we present a percolation theory for time-varying networks. We develop a renormalization group theory for a prototypical network on a regular grid, where individual links switch stochastically between active and inactive states. The question whether a given source node can communicate with a destination node along paths of active links is equivalent to a percolation problem. Our theory maps the temporal existence of multi-hop paths on an effective two-state Markov process. We show analytically how this Markov process converges towards a memory-less Bernoulli process as the hop distance between source and destination node increases. Our work extends classical percolation theory to the dynamic case and elucidates temporal correlations of message losses. Quantification of temporal correlations has implications for the design of wireless communication and control protocols, e.g. in cyber-physical systems such as self-organized swarms of drones or smart traffic networks.Comment: 8 pages, 3 figure

Parameters estimated in the multivariate logistic regression model.

<p>Parameters estimated in the multivariate logistic regression model.</p

Demographics and baseline characteristics for opioid groups by quartiles.

<p>Demographics and baseline characteristics for opioid groups by quartiles.</p

Odds ratios of recurrence and death for 1 unit opioid usage increase over all cancer stage.

<p>Odds ratios of recurrence and death for 1 unit opioid usage increase over all cancer stage.</p

Prevalence and risk factors for postoperative stress-related cardiomyopathy in adults

<div><p>Stress-related cardiomyopathy can develop during the postoperative period due to surgery-related stress factors. However, the prevalence and risk factors for this condition are not yet known. During a retrospective, observational study, patients older than 19 years who underwent procedures from January 2011 to December 2015 at a tertiary hospital were included. The main aim was to identify the prevalence and related risk factors for postoperative stress-related cardiomyopathy. To estimate the incidence per risk factor, univariate and multivariate Poisson regression analyses were performed. During the 5-year period, 95,840 patients older than 19 years underwent 125,314 procedures, and the prevalence of postoperative stress-related cardiomyopathy was 17.74 per 100,000 (95% confidence interval, 9.31–26.17), with an in-hospital mortality of 23.5%. As a result, three risk factors were significantly associated: preoperative American Society of Anesthesiologists classification (incidence rate ratio, 5.901 for American Society of Anesthesiologists class 1–2 [ref] versus 3–6; 95% confidence interval,1.289–27.002; <i>P</i> = 0.022); preoperative body mass index (incidence rate ratio, 1.247 for increases of 18.5 [ref] to 30; 95% confidence interval, 1.067–1.458; <i>P</i> = 0.006); and preoperative serum sodium (incidence rate ratio, 0.830 for each increase of 10 mmol/L from 130; 95% confidence interval, 0.731–0.942; <i>P</i> = 0.004). The incidence rate ratio for age for each increase of 10 years from 50 years was 1.057, but it was not statistically significant (<i>P</i> = 0.064). Our study found that the prevalence of postoperative stress-related cardiomyopathy was 17.74 patients per 100,000 adult patients over the course of 5 years, with four cases of in-hospital mortality. Factors that increased the risk of postoperative stress-related cardiomyopathy included higher American Society of Anesthesiologists class (≥3), preoperative hyponatremia, and higher preoperative body mass index.</p></div

Crude prevalence per 100,000 postoperative stress-related cardiomyopathies over the course of 5 years.

<p>ASA, American Society of Anesthesiologists; BUN, blood urea nitrogen; ICU, intensive care unit; IHD, ischemic heart disease; NU disease, neurologic disease; postop, postoperative.</p

Final risk factors for occurrence of postoperative SICMP using multivariate stepwise poisson regression analysis.

<p>Final risk factors for occurrence of postoperative SICMP using multivariate stepwise poisson regression analysis.</p