Spontaneous and information-induced bursting activities in honeybee hives.

Social entrainment is important for functioning of beehive organization. By analyzing a dataset of approximately 1000 honeybees (Apis mellifera) tracked in 5 trials, we discovered that honeybees exhibit synchronized activity (bursting behavior) in their locomotion. These bursts occurred spontaneously, potentially as a result of intrinsic bee interactions. The empirical data and simulations demonstrate that physical contact is one of the mechanisms for these bursts. We found that a subset of honeybees within a hive which become active before the peak of each burst, and we refer to these bees as "pioneer bees." Pioneer bees are not selected randomly, but rather, are linked to foraging behavior and waggle dancing, which may help spread external information in the hive. By using transfer entropy, we found that information flows from pioneer bees to non-pioneer bees, which suggest that the bursting behavior is caused by foraging behavior and spreading the information through the hive and promoting integrated group behavior among individuals.

Disordered beta thinned ensemble with applications.

It recently has been found that methods of the statistical theories of spectra can be a useful tool in the analysis of spectra far from levels of Hamiltonian systems. The purpose of the present study is to deepen this kind of approach by performing a more comprehensive spectral analysis that measures both the local- and long-range statistics. We have found that, as a common feature, spectra of this kind can exhibit a situation in which local statistics are relatively quenched while the long-range ones show large fluctuations. By combining three extensions of the standard random matrix theory (RMT) and considering long spectra, we demonstrate that this phenomenon occurs when disorder and level incompleteness are introduced in an RMT spectrum. Consequently, the long-range statistics follow Taylor's law, suggesting the presence of a fluctuation scaling (FS) mechanism in this kind of spectra. Applications of the combined ensemble are then presented for spectra originate from several very diverse areas, including complex networks, COVID-19 time series, and quantitative linguistics, which demonstrate that short- and long-range statistics reflect the rigid and elastic characteristics of a given spectrum, respectively. These observations may shed some light on spectral data classification.

Analysis of Xinjiang asphaltenes using high precision spectroscopy.

Asphaltenes are known for causing flow assurance problems in numerous oil fields. In this study we present a comparative spectroscopic analysis of Xinjiang heavy oil asphaltenes as part of ongoing research for an environmentally friendly and cheap chemical inhibitor. The goal is to predict the internal morphology of these asphaltenes through comparative analysis using high precision spectroscopy. Fourier transform infrared spectroscopy (FTIR), proton-nuclear magnetic resonance (H-NMR) and electrospray ionization Fourier transform ion cyclotron resonance combined with mass spectroscopy were used in this analysis. Several studies have demonstrated the enormous potential of these techniques to characterize hydrocarbons. Here we comparatively apply these techniques to characterize Xinjiang asphaltenes with reference to earlier imaging studies with atomic force and scanning tunneling microscopy to assign a structure to these asphaltenes. Results revealed the nature of the asphaltenes to be polycyclic, aromatic with both heteroatomic and metallic content. Thirteen basic and eleven non-basic/acidic nitrogen compounds fused within the aromatic network were identified. The mass distribution is in the range between 100-800 Da. H-NMR revealed various structural parameters (aromaticity and degree of unsaturation) and together with FTIR various functional groups were identified that include: ethers, sulphides, amides and sulfoxides. The predicted structures are consistent with the "island" and "aryl linked core" models.

Community detection in dynamic networks via adaptive label propagation.

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.

Multi-resolution community detection in massive networks.

Aiming at improving the efficiency and accuracy of community detection in complex networks, we proposed a new algorithm, which is based on the idea that communities could be detected from subnetworks by comparing the internal and external cohesion of each subnetwork. In our method, similar nodes are firstly gathered into meta-communities, which are then decided to be retained or merged through a multilevel label propagation process, until all of them meet our community criterion. Our algorithm requires neither any priori information of communities nor optimization of any objective function. Experimental results on both synthetic and real-world networks show that, our algorithm performs quite well and runs extremely fast, compared with several other popular algorithms. By tuning a resolution parameter, we can also observe communities at different scales, so this could reveal the hierarchical structure of the network. To further explore the effectiveness of our method, we applied it to the E-Coli transcriptional regulatory network, and found that all the identified modules have strong structural and functional coherence.

Stochastic Model for Phonemes Uncovers an Author-Dependency of Their Usage.

We study rank-frequency relations for phonemes, the minimal units that still relate to linguistic meaning. We show that these relations can be described by the Dirichlet distribution, a direct analogue of the ideal-gas model in statistical mechanics. This description allows us to demonstrate that the rank-frequency relations for phonemes of a text do depend on its author. The author-dependency effect is not caused by the author's vocabulary (common words used in different texts), and is confirmed by several alternative means. This suggests that it can be directly related to phonemes. These features contrast to rank-frequency relations for words, which are both author and text independent and are governed by the Zipf's law.

Explaining Zipf's law via a mental lexicon.

Zipf's law is the major regularity of statistical linguistics that has served as a prototype for rank-frequency relations and scaling laws in natural sciences. Here we show that Zipf's law-together with its applicability for a single text and its generalizations to high and low frequencies including hapax legomena-can be derived from assuming that the words are drawn into the text with random probabilities. Their a priori density relates, via the Bayesian statistics, to the mental lexicon of the author who produced the text.