Precise influence evaluation in complex networks

Evaluating node influence is fundamental for identifying key nodes in complex networks. Existing methods typically rely on generic indicators to rank node influence across diverse networks, thereby ignoring the individualized features of each network itself. Actually, node influence stems not only from general features but the multi-scale individualized information encompassing specific network structure and task. Here we design an active learning architecture to predict node influence quantitively and precisely, which samples representative nodes based on graph entropy correlation matrix integrating multi-scale individualized information. This brings two intuitive advantages: (1) discovering potential high-influence but weak-connected nodes that are usually ignored in existing methods, (2) improving the influence maximization strategy by deducing influence interference. Significantly, our architecture demonstrates exceptional transfer learning capabilities across multiple types of networks, which can identify those key nodes with large disputation across different existing methods. Additionally, our approach, combined with a simple greedy algorithm, exhibits dominant performance in solving the influence maximization problem. This architecture holds great potential for applications in graph mining and prediction tasks

A Pilot Study on Real-time Monitoring of Heart Rate and Movement Speed in Middle-distance Race of Physical Education Classes

In Chinese universities, students need to participate in the middle-distance-race. Normally, female students are required to participate in the race of 800 meters, while male students are required to participate in the race of 1000 meters. However, it is difficult for teachers to grasp the real time information of students during the race. And there is a lack of timely communications between the teachers and students. Focusing on this issue, this study, with the use of POLAR heart rate sensor and other modern information technologies, expands the original function of the sensor to achieve a concurrent operation of detecting heart rates and automatically measuring the movement speed. The researchers have successfully designed a micro system to monitor the process of middle-distance race. Moreover, the study also engages in a preliminary experiment verification so as to provide object and effective reference and basis for the middle-distance race physical education teaching in universities

Digital signal processor-based real-time optical Doppler tomography system

We present a real-time data-processing and display unit, based on a custom-designed digital signal processor (DSP) module for imaging tissue structure and Doppler blood flow. The DSP module is incorporated into a conventional optical coherence tomography system. We also demonstrate the flexibility of embedding advanced Doppler processing algorithms in the DSP module. Two advanced velocity estimation algorithms previously introduced by us are incorporated in this DSP module. Experiments on Intralipid flow demonstrate that a pulsatile flow of several hundred pulses per minute can be faithfully captured in M-scan mode by this DSP system. In vivo imaging of a rat's abdominal blood flow is also presented.Electrical and Computer Engineerin

Delay-Dependent Finite-Time H

Delay-dependent finite-time H∞ controller design problems are investigated for a kind of nonlinear descriptor system via a T-S fuzzy model in this paper. The solvable conditions of finite-time H∞ controller are given to guarantee that the loop-closed system is impulse-free and finite-time bounded and holds the H∞ performance to a prescribed disturbance attenuation level γ. The method given is the ability to eliminate the impulsive behavior caused by descriptor systems in a finite-time interval, which confirms the existence and uniqueness of solutions in the interval. By constructing a nonsingular matrix, we overcome the difficulty that results in an infeasible linear matrix inequality (LMI). Using the FEASP solver and GEVP solver of the LMI toolbox, we perform simulations to validate the proposed methods for a nonlinear descriptor system via the T-S fuzzy model, which shows the application of the T-S fuzzy method in studying the finite-time control problem of a nonlinear system. Meanwhile the method was also applied to the biological economy system to eliminate impulsive behavior at the bifurcation value, stabilize the loop-closed system in a finite-time interval, and achieve a H∞ performance level

Identifying the most influential roads based on traffic correlation networks

Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows. © 2019, The Author(s)

The Correctional Model of Population Development Equation

ABSTRACT The problem of population development has always been the key problem of restricting the development of our country. In order to increase the prediction accuracy, we analyze the exponential model, logistic model and continuous model. Also, the improved discrete population development model is provided to control the quantity and improve the quality of population

Cancellation of coherent artifacts in optical coherence tomography imaging

Coherent artifacts in optical coherence tomography ͑OCT͒ images can severely degrade image quality by introducing false targets if no targets are present at the artifact locations. Coherent artifacts can also add constructively or destructively to the targets that are present at the artifact locations. This constructive or destructive interference will result in cancellation of the true targets or in display of incorrect echo amplitudes of the targets. We introduce the use of a nonlinear deconvolution algorithm, CLEAN, to cancel coherent artifacts in OCT images of extracted human teeth. The results show that CLEAN can reduce the coherent artifacts to the noise background, sharpen the air-enamel and enamel-dentin interfaces, and improve the image contrast