A Survey of Information Entropy Metrics for Complex Networks

et al. 2022

Sci Rep

Neurophysiological measurements suggest that human information processing is evinced by neuronal activity. However, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear. We introduce and validate a mathematical model of the information processing capacity of a brain region in terms of neuronal activity, input storage capacity, and the arrival rate of afferent information. We applied the model to fMRI data obtained from a flanker paradigm in young and old subjects. Our analysis showed that—for a given cognitive task and subject—higher information processing capacity leads to lower neuronal activity and faster responses. Crucially, processing capacity—as estimated from fMRI data—predicted task and age-related differences in reaction times, speaking to the model’s predictive validity. This model offers a framework for modelling of brain dynamics in terms of information processing capacity, and may be exploited for studies of predictive coding and Bayes-optimal decision-making.

mentioning

confidence: 99%

Brain information processing capacity modeling

Zheng

et al. 2022

Sci Rep

“…Entropy [22][23][24] is widely used in thermodynamics to describe the process of heat conduction, and the essence of entropy is the internal chaos of the system. With the development of statistics and informatics, the meaning of entropy has been expanded.…”

Section: Local Structural Entropymentioning

confidence: 99%

Identifying Key Nodes in Complex Networks Based on Local Structural Entropy and Clustering Coefficient

Mathematical Problems in Engineering

Guangwu

et al. 2022

Key nodes have a significant impact, both structural and functional, on complex networks. Commonly used methods for measuring the importance of nodes in complex networks are those using degree centrality, clustering coefficient, etc. Despite a wide range of application due to their simplicity, their limitations cannot be ignored. The methods based on degree centrality use only first-order relations of nodes, and the methods based on the clustering coefficient use the closeness of the neighbors of nodes while ignore the scale of numbers of neighbors. Local structural entropy, by replacing the node influence on networks with local structural influence, increases the identifying effect, but has a low accuracy in the case of high clustered networks. To identify key nodes in complex networks, a novel method, which considers both the influence and the closeness of neighbors and is based on local structural entropy and clustering coefficient, is proposed in this paper. The proposed method considers not only the information of the node itself, but also its neighbors. The simplicity and accuracy of measurement improve the significance of characterizing the reliability and destructiveness of large-scale networks. Demonstrations on constructed networks and real networks show that the proposed method outperforms other related approaches.

“…Information entropy is related to the probability distribution of events. e more uniform the probability distribution, the greater the information entropy [29]. When all probabilities are equal, the larger the information entropy is.…”

Section: Calculation Of Crack Fractal Dimension and Crackmentioning

confidence: 99%

Effect of Recycled Bagasse on Cracking Behavior of Clay Materials

Liu

Advances in Materials Science and Engineering

et al. 2022

Bagasse is the residue after juicing sugarcane, and bagasse is a recyclable biological resource that can be used in many ways. Under arid climatic conditions, the clay material shrinks and loses water. Many crisscross drying shrinkage cracks formed on the surface and inside the soil will affect the stability of the soil. In this article, the cracking characteristics of clays with different bagasse contents during the evaporation process have been studied. The cracks were extracted and calculated by digital image processing technology, and the crack characteristics of samples with different bagasse contents were studied by fractal dimension and crack entropy. The results show that when moisture content maintains 34%, the clay material forms only one main crack without bagasse, forms no crack with bagasse content of 9%, and forms a crack network with the bagasse content of 3%. Adding bagasse to the clay can advance the cracking time of the clay, and different contents of bagasse have different effects on the cracking time of the clay. Among these five experimental groups, the clay was affected the most with 3% bagasse content, while the least effect happened with 6% bagasse content. The time can be advanced when the clay crack entropy appears and increases the size of the crack entropy.