Synchronization between Different Networks with Time-Varying Delay and Its Application in Bilayer Coupled Public Traffic Network

In order to study the dynamic characteristics of urban public traffic network, this paper establishes the conventional bus traffic network and the urban rail traffic network based on the space R modeling method. Then regarding these two networks as the subnetwork, the paper presents a new bilayer coupled public traffic network through the transfer relationship between subway and bus, and this model well reflects the connection between the passengers and bus operating vehicles. Based on the synchronization theory of coupling network with time-varying delay and taking “Lorenz system” as the network node, the paper studies the synchronization of bilayer coupled public traffic network. Finally, numerical results are given to show the impact of public traffic dispatching, delayed departure, the number of public bus stops between bus lines, and the number of transfer stations between two traffic modes on the bilayer coupled public traffic network balance through Matlab simulation

Direct solution of Navier-Stokes equations by using an upwind local RBF-DQ method

The differential quadrature (DQ) method is able to obtain quite accurate numerical solutions of differential equations with few grid points and less computational effort. However, the traditional DQ method is convenient only for regular regions and lacks upwind mechanism to characterize the convection of the fluid flow. In this paper, an upwind local radial basis function-based DQ (RBF-DQ) method is applied to solve the Navier-Stokes equations, instead of using an iterative algorithm for the primitive variables. The non-linear collocated equations are solved using the Levenberg-Marquardt method. The irregular regions of 2D channel flow with different obstructions situations are considered. Finally, the approach is validated by comparing the results with those obtained using the well-validated Fluent commercial package

Toward a direct measurement of the cosmic acceleration: The first observation of HI 21cm absorption line at FAST

In this work, we report the first result from the investgation of Neutral atomic hydrogen(HI) 21cm absorption line in spectrum of PKS1413+135 as a associated type at redshift $z\approx 0.24670041$ observed by FAST using the observing time of 10 minutes for the absorber and the spectral resolution of the raw data was setted to 10 Hz. The full spectral profile is analysed by fitting the absorption line with single Gaussian function as the resolution of 10kHz in 2MHz bandwidth, eventually intending to illustrate the latest cosmic acceleration by the direct measurement of time evolution of the redshift of HI 21cm absorption line with Hubble flow toward a same background Quasar in the time interval of more than a decade or many years as a detectable signal that produced by the accelerated expansion of the Universe in the era of FAST at low redshift space,namely redshift drift $\dot{z}$ or SL effect. The obtained HI gas column density $\rm N_{HI} \approx 2.2867\times 10^{22}/cm^2$ of this DLA system, much equivalent to the originally observed value $\rm N_{HI} \approx 1.3\times 10^{19}\times(T_s/f)/cm^2$ within the uncertainties of the spin temperature of a spiral host galaxy, and the signal to noise ratio SNR highly reaching 57.4357 for the resolution of 10kHz evidently validates the opportunities of the HI 21cm absorption lines of DLA systems to enforce the awareness of the physical motivation of dark energy by the probe of $\rm\dot{z}$ with the enhancement of accuracy in the level of $\sim 10^{-10}$ per decade.Comment: 26 pages,8 figures, 3 tables, submitted to JCA

Macro- and microphysical characteristics of snowfall and non-snowfall clouds in the West Tianshan Mountains of China based on cloud radar

A Correction to this article was published on 10 February 2023. https://doi.org/10.1007/s00703-023-00953-6The macro- and microphysical characteristics of wintertime precipitating clouds and non-precipitating clouds over the West Tianshan Mountains, China, were analyzed with the use of Ka-band radar and weighing rain gauge observations. The data were collected from January to February 2019, December 2019, and from December 2020 to February 2021. Snowfall clouds mainly ranged from 0.15 similar to 2.50 km and had a reflectivity (Z) of mostly 10 33 dBZ. Non-snowfall clouds were primarily distributed within the height range of 2 similar to 8 km, and the Z values were within the range of - 22 similar to 15 dBZ. Compared with non-snowfall clouds, snowfall clouds have a higher particle water content (M) but a similar radial velocity (V). Light and moderate snowfall clouds were mainly located at heights of 0.15 similar to 3.50 km and had Z values concentrated from 5 similar to 24 dBZ. Heavy snowfall clouds were characterized by a Z of 5 similar to 30 dBZ below 3.5 km. The proportion of clouds with an M value> 0.1 g.m(-3) below 2 km was noticeably higher for heavy snow events than for light and moderate snow events. The differences in the distributions and values of snowfall cloud V values were small among the different snow types, and descending motions occurred below 6 km, with V ranging - 1.4 similar to - 0.3 m.s(-1). The heights of the non-snowfall cloud top and base during the day were lower than those at night. The snowfall cloud top did not show noticeable diurnal variations. The cloud top and base heights of the non-snowfall clouds both showed a single-peak distribution. The cloud top values of snowfall clouds exhibited bimodal distributions.Peer reviewe

Supervised anomaly detection in uncertain pseudoperiodic data streams

Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this paper, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts an efficient uncertainty pre-processing procedure to identify and eliminate uncertainties in data streams. Based on the corrected data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on a number of real datasets