Wellshaney0776

Within data gathered through passive monitoring of patients with Multiple Sclerosis (MS), there is a clear necessity for improved methodological approaches to match the emergence of continuous, objective, measuring technologies. As most gold standards measure infrequently and require clinician presence, fluctuations in the daily progression are not accounted for. Due to the underlying conditions of homogeneity and stationarity (the main tenets of ergodicity) not being met for the majority of the statistical methods employed in the clinical setting, alternative approaches should be investigated. A solution is to use a non-linear time series analysis approach. Here, Early-Warning Signals (EWS) in the form of critical fluctuations in Keystroke Dynamics (KD), collected using participant's smartphones, are investigated as indicators for a clinical change in three groups. These are patients with MS and changes in Magnetic Resonance Imaging (MRI), patients with MS but without changes in MRI, and healthy controls (HCs). Here, we report examples of EWS and changes in KD coinciding with clinically relevant changes in outcome measures in both patients with and without differences in the amount of MRI enhancing lesions. We also report no clinically relevant changes in EWS in the HC population. This study is a first promising step toward using EWS to identify periods of instability as measured by a continuous objective measure as a proxy for outcome measures in the field of MS.We consider the KdV equation on a circle and its Lie-Poisson reconstruction, which is reminiscent of an equation of motion for fluid particles. For periodic waves, the stroboscopic reconstructed motion is governed by an iterated map whose Poincaré rotation number yields the drift velocity. We show that this number has a geometric origin it is the sum of a dynamical phase, a Berry phase, and an "anomalous phase." The last two quantities are universal they are solely due to the underlying Virasoro group structure. The Berry phase, in particular, was previously described by Oblak [J. High Energy Phys. 10, 114 (2017)] for two-dimensional conformal field theories and follows from adiabatic deformations produced by the propagating wave. We illustrate these general results with cnoidal waves, for which all phases can be evaluated in closed form thanks to a uniformizing map that we derive. Along the way, we encounter "orbital bifurcations" occurring when a wave becomes non-uniformizable there exists a resonance wedge, in the cnoidal parameter space, where particle motion is locked to the wave, while no such locking occurs outside of the wedge.The propagation of failures and blackouts in electric networks is a complex problem. Typical models, such as the ORNL-PSerc-Alaska (OPA), are based on a combination of fast and slow dynamics. The first describes the cascading failures while the second describes the grid evolution through line and generation upgrades as well as demand growth, all taking place in time scales from days to years. The growing integration of renewable energy sources, whose power fluctuates in time scales from seconds to hours, together with the increase in demand, which also presents fast fluctuations, requires the incorporation of distributed methods of control in the demand side to avoid the high cost of ordinary control in conventional power plants. In this work, we extend the OPA model to include fluctuations in the demand at time scales of the order of minutes, intraday demand variations, and the effect of demand control. We find that demand control effectively reduces the number of blackouts without increasing the probability of large-scale events.The purpose of this study is to discriminate sunflower seeds with the help of a dataset having spectral and textural features. The production of crop based on seed purity and quality other hand sunflower seed used for oil content worldwide. In this regard, the foundation of a dataset categorizes sunflower seed varieties (Syngenta CG, HS360, S278, HS30, Armani, and High Sun 33), which were acquired from the agricultural farms of The Islamia University of Bahawalpur, Pakistan, into six classes. For preprocessing, a new region-oriented seed-based segmentation was deployed for the automatic selection of regions and extraction of 53 multi-features from each region, while 11 optimized fused multi-features were selected using the chi-square feature selection technique. For discrimination, four supervised classifiers, namely, deep learning J4, support vector machine, random committee, and Bayes net, were employed to optimize the multi-feature dataset. We observe very promising accuracies of 98.2%, 97.5%, 96.6%, and 94.8%, respectively, when the size of a region is (180 × 180).We have analyzed the electrocardiographic data collected during continuous 7-day ambulatory recordings in patients with frequent premature ventricular complexes (PVCs). We analyze the dependence of the frequency and patterns of PVCs on the heart rate and the time of the day. Patients display rhythms of a complex yet consistent structure. In a given patient, the pattern remains robust over different days and particular repetitive patterns appear at specific heart rates, suggesting the appearance of bifurcations in the dynamics. Over the course of 24 h, we find that in some patients, patterns appear to depend only on the heart rate, whereas in others, both the time of the day and the heart rate play a role in controlling the dynamics. Identifying parameter values at which bifurcations occur facilitates the development of dynamical models for arrhythmia. The use of powerful recording and analysis techniques will enable improved analysis of data and better understanding of mechanisms of arrhythmia in individual patients.The ordinal patterns of time series provide a variety of information about the dynamic characteristics of the underlying process. SN38 With the ordinal symbolic approach, recently, a permutation-based Hill's diversity index has been proposed as a useful tool for complex system analysis. However, this method just measures the complexity of the system from the perspective of uncertainty, while the underlying structural information of the system dynamics can hardly be captured. To overcome this deficiency, this paper introduces a Hill-index complexity measure (HICM). The numerical applications suggest that the proposed HICM has greater discriminating power than the conventional statistical complexity measure (SCM). With parameter r, even between chaotic systems with extremely similar dynamics, HICM can make a clear differentiation, which can barely be achieved by SCM. Besides, HICM with appropriate r is relatively more robust against noise than SCM. In the empirical application to financial markets, the approach adopted in this paper could differentiate the stage of stock market development.