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Based on the python networkx toolbox analysis of the trade network structure form an information flow perceptive, studies show that China's import and export trade with some areas along the \"The Belt and Road Initiative\" has declined, but overall, whether it is Both import demand and export demand are rising year by year, which shows that China and the various regions along the \"The Belt and Road Initiative\" line have become closer in terms of trade and international trade trends have become more apparent.

The article considers the problem of optimization of telecommunication systems based on improving the dynamics of information flows. This task is formulated on the basis of a systematic approach, which consists in the fact that a multidimensional indicator is chosen as a system indicator, taking into account not only the connections of elements, but also the degree of use of these connections in the functioning of the network. This allows you to distribute information flows by changing the elements of the route matrix in the model of an open queuing network in order to optimize the system. It is shown that it is expedient to solve the optimization problem by applying the developed algorithm for the general solution of the problem. Algorithm procedures for the dynamics of information flows imply the transfer of part of the load of the elements that are bottlenecks to other elements of the system. The performance of the algorithm is confirmed by an example.

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG‐based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG‐based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time‐based, and frequency‐based or time‐frequency‐based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented. This article reviews EEG‐based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric, or nonparametric, time‐based, frequency‐based or time‐frequency‐based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

We study the thermodynamic properties induced by non-reciprocal interactions between stochastic degrees of freedom in time- and space-continuous systems. We show that, under fairly general conditions, non-reciprocal coupling alone implies a steady energy flow through the system, i.e., non-equilibrium. Projecting out the non-reciprocally coupled degrees of freedom renders non-Markovian, one-variable Langevin descriptions with complex types of memory, for which we find a generalized second law involving information flow. We demonstrate that non-reciprocal linear interactions can be used to engineer non-monotonic memory, which is typical for, e.g., time-delayed feedback control, and is automatically accompanied with a nonzero information flow through the system. Furthermore, already a single non-reciprocally coupled degree of freedom can extract energy from a single heat bath (at isothermal conditions), and can thus be viewed as a minimal version of a time-continuous, autonomous 'Maxwell demon'. We also show that for appropriate parameter settings, the non-reciprocal system has characteristic features of active matter, such as a positive energy input on the level of the fluctuating trajectories without global particle transport.

On social media, information spreads quickly and can affect other users. In order for information to spread to many users, it is important to know how the information diffusion model or the flow of information is and who is the influencer on social media. In this paper, the Information Diffusion Model in social media was developed using the Continuous Time Markov Chain (CTMC). The tweet-retweet activity of social media users such as on Twitter can be seen as the CTMC model, because it fulfills the nature of Markov, i.e. retweets from subsequent users depend only on the current user's retweet and do not depend on the retweet history of previous users. Users engaged in retweet tweets are state of CTMC. By using the tweet-retweet network simulation data including 10 users and 4 topics, the influencer rankings were obtained. An influencer rating is determined not only by the number of retweets, but also by the time it takes to get those retweets.

People must integrate disparate sources of information when making decisions, especially in social contexts. But information does not always flow freely. It can be constrained by social networks 1 – 3 and distorted by zealots and automated bots 4 . Here we develop a voter game as a model system to study information flow in collective decisions. Players are assigned to competing groups (parties) and placed on an ‘influence network’ that determines whose voting intentions each player can observe. Players are incentivized to vote according to partisan interest, but also to coordinate their vote with the entire group. Our mathematical analysis uncovers a phenomenon that we call information gerrymandering: the structure of the influence network can sway the vote outcome towards one party, even when both parties have equal sizes and each player has the same influence. A small number of zealots, when strategically placed on the influence network, can also induce information gerrymandering and thereby bias vote outcomes. We confirm the predicted effects of information gerrymandering in social network experiments with n  = 2,520 human subjects. Furthermore, we identify extensive information gerrymandering in real-world influence networks, including online political discussions leading up to the US federal elections, and in historical patterns of bill co-sponsorship in the US Congress and European legislatures. Our analysis provides an account of the vulnerabilities of collective decision-making to systematic distortion by restricted information flow. Our analysis also highlights a group-level social dilemma: information gerrymandering can enable one party to sway decisions in its favour, but when multiple parties engage in gerrymandering the group loses its ability to reach consensus and remains trapped in deadlock. In a voter game, information gerrymandering can sway the outcome of the vote towards one party, even when both parties have equal sizes and each player has the same influence; and this effect can be exaggerated by strategically placed zealots or automated bots.

Cells use spatial constraints to control and accelerate the flow of information in enzyme cascades and signalling networks. Synthetic silicon-based circuitry similarly relies on spatial constraints to process information. Here, we show that spatial organization can be a similarly powerful design principle for overcoming limitations of speed and modularity in engineered molecular circuits. We create logic gates and signal transmission lines by spatially arranging reactive DNA hairpins on a DNA origami. Signal propagation is demonstrated across transmission lines of different lengths and orientations and logic gates are modularly combined into circuits that establish the universality of our approach. Because reactions preferentially occur between neighbours, identical DNA hairpins can be reused across circuits. Co-localization of circuit elements decreases computation time from hours to minutes compared to circuits with diffusible components. Detailed computational models enable predictive circuit design. We anticipate our approach will motivate using spatial constraints for future molecular control circuit designs. Fast and scalable molecular logic circuits can be created through the spatial organization of DNA hairpins on DNA origami scaffolds.

During flexible behavior, multiple brain regions encode sensory inputs, the current task, and choices. It remains unclear how these signals evolve. We simultaneously recorded neuronal activity from six cortical regions [middle temporal area (MT), visual area four (V4), inferior temporal cortex (IT), lateral intraparietal area (LIP), prefrontal cortex (PFC), and frontal eye fields (FEF)] of monkeys reporting the color or motion of stimuli. After a transient bottom-up sweep, there was a top-down flow of sustained task information from frontoparietal to visual cortex. Sensory information flowed from visual to parietal and prefrontal cortex. Choice signals developed simultaneously in frontoparietal regions and travelled to FEF and sensory cortex. This suggests that flexible sensorimotor choices emerge in a frontoparietal network from the integration of opposite flows of sensory and task information.

The article provides a comparative analysis of the methods of teaching mathematics to university students of the traditional face-to-face form of education and in the form of distance learning using electronic teaching aids. To compare these forms the main elements that make up the teaching methodology were highlighted: teaching methods, levels of mastering the content of education, the type of educational situation, types of feedback. Each element of the methodology was presented in the form of a hierarchical subsystem performing teaching functions at different levels of assimilation. In the course of the conducted polls of the students and teachers it was found out that the traditional teaching methodology worsened its didactic properties with the transition to a distance form. Therefore, in order to increase the effectiveness of teaching it is necessary to transfer the methodology to new principles of cyberpedagogy and create a closed educational process with a directed information flow.

The paper presents conceptual framework and information model of intelligent decision support based on traces of user digital lives and ontology-based user categorisation. The conceptual framework defines components that provide information revealed from the traces of user digital lives, generalize this information, and make ontology inference. The information model defines information flows between the components of the conceptual framework. The novelties of this research are grouping users with common preferences and decision making behaviours based on the user digital traces, and context-sensitive ontology-based categorization of users into the user groups.