ABSTRACT
Analyzing and obtaining useful information is challenging when facing a new complex system. Traditional methods often focus on specific structural aspects, such as communities, which may overlook the important features and result in biased conclusions. To address this, this article suggests an adaptive algorithm for exploring complex system structures using a generative model. This method calculates and optimizes node parameters, which can reflect the latent structural characteristics of the complex system. The effectiveness and stability of this method have been demonstrated in comparative experiments on 10 sets of benchmark networks using our model parameter configuration scheme. To enhance adaptability, algorithm fusion strategies were also proposed and tested on two real-world networks. The results indicate that the algorithm can uncover multiple structural features, including clustering, overlapping, and local chaining. This adaptive algorithm provides a promising approach for exploring complex system structures.
ABSTRACT
One misprint in our manuscript is reported and corrected.
ABSTRACT
The smoke of burning mosquito-repellent incense was taken as an example for the local air pollution to be detected and analyzed in situ and in real time. And the spectra of the ambient air, human breathing, and smoke were detected in situ with the LIBS technique. There are some additional spectral lines being found in human breathing, such as the C, Hß line, and the CN molecular bands. Some characteristic peaks of the elements Fe, Ca, Ti, Sr, and Cr have been observed in the smoke. Moreover, the vibrational and rotational temperature of the CN molecule were calculated. The mosquito-repellent incense was dipped into the solutions containing Mn and Pb to simulate heavy metal pollution in the atmosphere.
