Discrete Dynamics in Nature and Society

Edge networking brings computation and data storage as close to the point of request as possible. Various intelligent devices are connected to the edge nodes where traffic packets flow. Traffic classification tasks are thought to be a keystone for network management; researchers can analyze packets captured to understand the traffic as it hits their network. However, the existing traffic classification framework needs to conduct a unified analysis, which leads to the huge bandwidth resources required in the process of transferring all captured packet files to train a global classifier. In this paper, a semisupervised graph neural network traffic classifier is proposed for cloud-edge architecture so that cloud servers and edge nodes could cooperate to perform the traffic classification tasks in order to deliver low latency and save bandwidth on the edge nodes. To preserve the structural information and interrelationships conveyed in packets within a session, we transform traffic sessions into graphs. We segment the frequently combined consecutive packets into granules, which are later transformed into the nodes in graphs. Edges could extract the adjacency of the granules in the sessions; the edge node side then selects the highly representative samples and sends them to the cloud server; the server side uses graph neural networks to perform semisupervised classification tasks on the selected training set. Our method has been trained and tested on several datasets, such as the VPN-nonVPN dataset, and the experimental results show good performance on accuracy, recall, and F-score.

Channel selection is a critical trade-off for digital products firms whose products are characterized by network externality. This work develops the models of consumers’ utility impacted by the network externality for two channel strategies of the digital product firms in the two-sided market: direct channel strategy and platform channel strategy. Deriving from the consumers’ utility, the optimization models of the two channel strategies are presented. The optimization models are solved through the Lagrangian method, and the comparative statics analysis is conducted to investigate the effect of network externality on optimality. Mathematical results show that if the intensity of network externality in the online platform surpasses that in the direct channel, the platform channel strategy dominates the other channel strategy; otherwise, the direct channel strategy is the firms’ optimal decision. In addition, the two channels share the equal optimal price, and the firms’ profit (and demand) would be positively impacted by the network effect and the products’ features but negatively impacted by the consumers’ learning cost. This work provides decision support for the digital product firms on channel selection in the context of the two-sided market.

The advent of the COVID-19 pandemic has markedly affected energy valuations and financial markets. As such, this article aims to scrutinize the dynamic interplay between stock market returns and crude oil prices, with a particular focus on China, factoring in the second-moment effect of volatility spillover. Employing an EGARCH process to model the leverage impact on returns’ volatility, the analysis utilizes daily data spanning from January 30, 2020, to August 30, 2022, and incorporates causality-in-mean and variance assessments. Empirical findings indicate that the QDII-LOF benchmark, representing oil prices, exerts a substantial influence on stock market returns. Nevertheless, the complete sample reveals no discernible spillover effects attributable to oil price fluctuations. These insights imply that the Chinese government’s actions should carefully weigh the ramifications of spillovers. Concurrently, investors are advised to attentively monitor the crude oil market when making portfolio allocation decisions.

In the context of technological innovation promoting the long-term sustainable development of enterprises, how to better motivate senior executives to create greater value for an enterprise is being widely discussed. In particular, the COVID-19 outbreak has raised concerns about whether companies can deliver more value by holding large amounts of cash. However, although scholars have conducted a lot of research on topics such as innovation and firm value, how differentiated executive compensation incentives regulate the relationship between firm innovation and the value of cash holdings has hardly been explored. This paper selects the balanced panel data of 1470 A-share listed companies from 2012 to 2020 in China to explore the relationship between innovation investment, executive compensation, and the value of cash holdings. It is found that innovation investment has a positive impact on the value of the cash holdings. Based on Herzberg’s hygiene motivational factors, different types of executive compensation may have a hygiene effect or a motivational effect, which is different. As a result, the moderating effect of executive compensation on innovation investment and the value of cash holdings are significantly different. Executive equity compensation and in-service consumption are motivational attributes. They have a positive moderating effect on innovation investment and the value of cash holdings. The moderating effect of executive monetary compensation on innovation investment and the value of cash holdings changes with the change in monetary compensation. When monetary compensation is lower than the threshold value, monetary compensation is reflected as a hygiene attribute, so it has no significant positive moderating effect on innovation investment and the value of cash holdings. When monetary compensation is higher than the threshold value, monetary compensation is reflected as a motivational attribute, so it has a significant positive moderating effect on innovation investment and the value of cash holdings. Meanwhile, it is tested that monetary compensation is not manipulated by executive compensation defense behavior when it is reflected as motivational attributes.

In this paper, we analyze the stability of the family of iterative methods designed by Jarratt using complex dynamics tools. This allows us to conclude whether the scheme known as Jarratt’s method is the most stable among all the elements of the family. We deduce that classical Jarratt’s scheme is not the only stable element of the family. We also obtain information about the members of the class with chaotical behavior. Some numerical results are presented for confirming the convergence and stability results.

This paper introduces the stability problems of Cohen–Grossberg type BAM neural network (BAMCGNN) with piecewise constant argument (PCA). By employing the homeomorphism theory, sufficient conditions for the existence and uniqueness of the equilibrium point are obtained; using inequality technique and Lyapunov method, sufficient stability criteria for BAMCGNN with PCA are presented. Finally, a numerical case shows the significance of the results of this paper.