Extensive experiments on different networks show that NCD-CEA has a competitive performance in solving RAPs. This article advances toward controlling virus spreading over large-scale networks.A well-known problem with distance-based formation control is the existence of multiple equilibrium points not associated with the desired formation. This problem can be potentially mitigated by introducing an additional controlled variable. In this article, we generalize the distance + angle-based scheme for 2-D formations of single-integrator agents by using directed graphs and triangulation of the n-agent formation. We show that under certain conditions on the control gains and desired formation shape, our controller ensures the asymptotic stability of the correct formation for almost all initial agent positions.This article proposes a memory-based event-triggering H∞ load frequency control (LFC) method for power systems through a bandwidth-constrained open network. To overcome the bandwidth constraint, a memory-based event-triggered scheme (METS) is first proposed to reduce the number of transmitted packets. Compared with the existing memoryless event-triggered schemes, the proposed METS has the advantage to utilize series of the latest released signals. To deal with the random deception attacks induced by open networks, a networked power system model is well established, which couples the effects of METS and random deception attacks in a unified framework. Then, a sufficient stabilization criterion is derived to obtain the memory H∞ LFC controller gains and event-triggered parameters simultaneously. Compared with existing memoryless LFC, the control performance is greatly improved since the latest released dynamic information is well utilized. Finally, an illustrative example is used to show the effectiveness of the proposed method.Transcutaneous cervical vagal nerve stimulation (tcVNS) devices are attractive alternatives to surgical implants, and can be applied for a number of conditions in ambulatory settings, including stress-related neuropsychiatric disorders. Transferring tcVNS technologies to at-home settings brings challenges associated with the assessment of therapy response. The ability to accurately detect whether tcVNS has been effectively delivered in a remote setting such as the home has never been investigated. We designed and conducted a study in which 12 human subjects received active tcVNS and 14 received sham stimulation in tandem with traumatic stress, and measured continuous cardiopulmonary signals including the electrocardiogram (ECG), photoplethysmogram (PPG), seismocardiogram (SCG), and respiratory effort (RSP). We extracted physiological parameters related to autonomic nervous system activity, and created a feature set from these parameters to 1) detect active (vs. sham) tcVNS stimulation presence with machine learning methods, and 2) determine which sensing modalities and features provide the most salient markers of tcVNS-based changes in physiological signals. Heart rate (ECG), vasomotor activity (PPG), and pulse arrival time (ECG+PPG) provided sufficient information to determine target engagement (compared to sham) in addition to other combinations of sensors. resulting in 96% accuracy, precision, and recall with a receiver operator characteristics area of 0.96. Two commonly utilized sensing modalities (ECG and PPG) that are suitable for home use can provide useful information on therapy response for tcVNS. The methods presented herein could be deployed in wearable devices to quantify adherence for at-home use of tcVNS technologies.The seismocardiogram (SCG) measures the movement of the chest wall in response to underlying cardiovascular events. Though this signal contains clinically-relevant information, its morphology is both patient-specific and highly transient. In light of recent work suggesting the existence of population-level patterns in SCG signals, the objective of this study is to develop a method which harnesses these patterns to enable robust signal processing despite morphological variability. Specifically, we introduce seismocardiogram generative factor encoding (SGFE), which models the SCG waveform as a stochastic sample from a low-dimensional subspace defined by a unified set of generative factors. We then demonstrate that during dynamic processes such as exercise-recovery, learned factors correlate strongly with known generative factors including aortic opening (AO) and closing (AC), following consistent trajectories in subspace despite morphological differences.  https://www.selleckchem.com/products/gs-9973.html  Furthermore, we found that changes in sensor location affect the perceived underlying dynamic process in predictable ways, thereby enabling algorithmic compensation for sensor misplacement during generative factor inference. Mapping these trajectories to AO and AC yielded R2 values from 0.81-0.90 for AO and 0.72-0.83 for AC respectively across five sensor positions. Identification of consistent behavior of SCG signals in low dimensions corroborates the existence of population-level patterns in these signals; SGFE may also serve as a harbinger for processing methods that are abstracted from the time domain, which may ultimately improve the feasibility of SCG utilization in ambulatory and outpatient settings.This study was to assess the feasibility of using non-standardized single-lead electrocardiogram (ECG) monitoring to automatically detect atrial fibrillation (AF) with special emphasis on the combination of deep learning based algorithm and modified patch-based ECG lead. Fifty-five consecutive patients were monitored for AF in around 24 hours by patch-based ECG devices along with a standard 12-lead Holter. Catering to potential positional variability of patch lead, four typical positions on the upper-left chest were proposed. For each patch lead, the performance of automated algorithms with four different convolutional neural networks (CNN) was evaluated for AF detection against blinded annotations of two clinicians. A total of 349,388 10-second segments of AF and 161,084 segments of sinus rhythm were detected successfully. Good agreement between patch-based single-lead and standard 12-lead recordings was obtained at the position MP1 that corresponds to modified lead II, and a promising performance of the automated algorithm with an R-R intervals based CNN model was achieved on this lead in terms of accuracy (93.