Sleep apnea has been recognized as a factor predisposing to atrial fibrillation recurrence and progression. The effect of other sleep-disturbing conditions on atrial fibrillation progression is not known. We sought to determine whether frequent periodic leg movement during sleep is a risk factor for progression of atrial fibrillation. In this retrospective study, patients with atrial fibrillation and a clinical suspicion of restless legs syndrome who were referred for polysomnography were divided into two groups based on severity of periodic leg movement during sleep: frequent (periodic movement index >35/h) and infrequent (≤35/h). Progression of atrial fibrillation to persistent or permanent forms between the two groups was compared using Wilcoxon rank-sum test, chi-square tests and logistic regression analysis. Of 373 patients with atrial fibrillation (77% paroxysmal, 23% persistent), 108 (29%) progressed to persistent or permanent atrial fibrillation during follow-up (median, 33 months; interquartile range, 16-50). Compared to patients with infrequent periodic leg movement during sleep (n=168), patients with frequent periodic leg movement during sleep (n=205) had a higher rate of atrial fibrillation progression (23% vs. 34%; p=0.01). Patients with frequent periodic leg movement during sleep were older and predominantly male; however, there were no significant differences at baseline in clinical factors that promote atrial fibrillation progression between both groups. On multivariate analysis, independent predictors of atrial fibrillation progression were persistent atrial fibrillation at baseline, female gender, hypertension and frequent periodic leg movement during sleep. In patients with frequent periodic leg movement during sleep, dopaminergic therapy for control of leg movements in patients with restless legs syndrome reduced risk of atrial fibrillation progression. Frequent leg movement during sleep in patients with restless legs syndrome is associated with progression of atrial fibrillation to persistent and permanent forms.

No abstract

In smart manufacturing, production machinery and auxiliary devices, referred to as industrial Internet of things (IIoT), are connected to a unified networking infrastructure for management and command deliveries in a precise production process. However, providing autonomous, reliable, and real-time offloaded services for such a production is an open challenge since these IIoT devices are assumed lightweight embedded platforms with limited computing performance. In this paper, we propose a pattern-identified online task scheduling (PIOTS) mechanism for the networking infrastructure, where multitier edge computing is provided, in order to handle the offloaded tasks in real time. First, historical IIoT task patterns in every timeslot are used to train a self-organizing map (SOM), which represents the features of the task patterns within defined dimensions. Consequently, offline task scheduling among edge computing-enabled entities is performed on the set of all SOM neurons using the Hungarian method to determine the expected optimal task assignments. In real-time context, whenever a task arrives at the infrastructure, the expected optimal assignment for the task is scheduled to the appropriate edge computing-enabled entity. Numerical simulation results show that the proposed PIOTS mechanism overcomes existing solutions in terms of computation performance and service capability.

This paper presents an evaluation method for a 1 mm coaxial calibration kit that can be used from DC to 110 GHz. The analytical model for the calibration kit was revisited and verified by comparing it with the electromagnetic High-Frequency Structure Simulator (HFSS). We also proposed a method to measure or appropriately estimate the physical parameters of the analytic model. This approach calculates the uncertainty based on the physical parameters, so that the uncertainty can be appropriately propagated to different measured quantities based on the covariance between all frequencies, including the real and imaginary parts. To verify the proposed method, a commercially available 1 mm calibration kit was evaluated, and the impedance of a device under test was measured using the evaluated kit. We compared the measured results with those of the National Institute of Standards and Technology (NIST) and confirmed that they agreed well with each other within the uncertainty. Additionally, the multiple reflections caused by the impedance mismatch between the signal source and the instrument was corrected, and its calibrated uncertainty was obtained in the time domain. Thus, the uncertainty of the impedance measurement in the frequency domain was properly propagated to the time domain.