Complexity最新文献

In the context of rapid development of social networks, users gather together through social network platforms (SNPs) to form social circles, which become the main carriers for users to disseminate public opinion information and emotions, and it is easy to form the phenomenon of network public opinion polarization in social circles. To prevent the polarization of network public opinions in social circles, the application of blockchain technology to SNPs can provide a credible database for the traceability of public opinions and avoid the spread of false information in social circles. Based on blockchain traceability, this paper establishes a three-party evolutionary game model consisting of government, SNPs, and social circle managers (SCMs). Through model solving and numerical simulation, the influencing factors of each party’s strategy are analyzed. The findings suggest: (1) Although the rising cost of building blockchain platforms may undermine the government’s incentive to regulate, the government will still prioritise support for their construction if they can significantly enhance social welfare. (2) The government could boost SNPs’ willingness to adopt blockchain technology through subsidy measures, but increased privacy risks could undermine this willingness. (3) Blockchain technology realizes the traceability of public opinion and improves the rational guidance tendency of SCMs. Therefore, in order to promote and strengthen the collaborative governance of network public opinion in social circles, SNPs should enhance the use of blockchain technology, while the government should give full play to its regulatory and supervisory roles. This study introduces blockchain technology for public opinion traceability; explores a collaborative network public opinion governance system based on strict regulation by the government, introduction of blockchain technology by SNPs, and rational guidance by SCMs; expands the related research on network public opinion governance; and provides a new idea for the study of network public opinion governance of social circles based on blockchain traceability.

Correct detection of plant diseases is critical for enhancing crop yield and quality. Conventional methods, such as visual inspection and microscopic analysis, are typically labor-intensive, subjective, and vulnerable to human error, making them infeasible for extensive monitoring. In this study, we propose a novel technique to detect tomato leaf diseases effectively and efficiently through a pipeline of four stages. First, image enhancement techniques deal with problems of illumination and noise to recover the visual details as clearly and accurately as possible. Subsequently, regions of interest (ROIs), containing possible symptoms of a disease, are accurately captured. The ROIs are then fed into K-means clustering, which can separate the leaf sections based on health and disease, allowing the diagnosis of multiple diseases. After that, a hybrid feature extraction approach taking advantage of three methods is proposed. A discrete wavelet transform (DWT) extracts hidden and abstract textures in the diseased zones by breaking down the pixel values of the images to various frequency ranges. Through spatial relation analysis of pixels, the gray level co-occurrence matrix (GLCM) is extremely valuable in delivering texture patterns in correlation with specific ailments. Principal component analysis (PCA) is a technique for dimensionality reduction, feature selection, and redundancy elimination. We collected 9014 samples from publicly available repositories; this dataset allows us to have a diverse and representative collection of tomato leaf images. The study addresses four main diseases: curl virus, bacterial spot, late blight, and Septoria spot. To rigorously evaluate the model, the dataset is split into 70%, 10%, and 20% as training, validation, and testing subsets, respectively. The proposed technique was able to achieve a fantastic accuracy of 99.97%, higher than current approaches. The high precision achieved emphasizes the promising implications of incorporating DWT, PCA, GLCM, and ANN techniques in an automated system for plant diseases, offering a powerful solution for farmers in managing crop health efficiently.

Some complex systems (e.g., an ecosystem) in direct contact with an environment can be assigned the temperature of the environment. Other complex systems, such as human beings, can maintain a core temperature of 36.5°C in environments with different temperatures, at least for a short period of time. Finally, for complex systems such as financial markets, whose environments we understand very little of, is there even a reasonable way to define a temperature? It is clear that human beings are almost never in thermal equilibrium with their surroundings, but can financial markets achieve detailed balance independently at all scales, or is information flow in such systems different at different scales? If we combine the information-theoretic picture with the thermodynamics picture of entropy, temperature is the driving force for changes in information content of a system. From an interactions point of view, the information content of a financial market can be computed from the cross correlations between its stocks. In their 2015 paper, Ye et al. (2015) constructed the normalized graph Laplacians in different time periods based on strong cross correlations between stocks listed on the New York Stock Exchange. By writing the partition function in terms of polynomials of the normalized graph Laplacian, Ye et al. computed the average energy E, entropy S, and inverse temperature β = 1/k_BT. This led us to an information-based definition of the inverse temperature. In this work, we investigated the inverse temperature β(ϵ,  n) at different times n and scales ϵ for two mature financial markets, using the S&P 500 and Nikkei 225 cross sections of stocks from January 2007 to May 2023. In the dynamics of β, the most prominent features are peaks at various times. We identified five esoteric and seven characteristic peaks and studied how they change with scale ϵ. The latter consists of a negative power-law dip followed by a positive power-law rise, with exponents narrowly distributed between 0.3–0.4. In addition, we constructed heat maps of β that reveal positive-, negative-, and infinite-slope cascades that hint at their possible exogenous and endogenous origins. Notably, the heat map of β confirmed that the 2007−2009 Global Financial Crisis was an endogenous crash in the US market, which in turn caused an exogenous crash in the Japanese stock market. To better understand the evolution of β, we analyzed ΔJ (the difference in the number of links) and ΔQ (the difference in the number of triangles) and found they oscillate in time. Occasionally, very intense swings of ΔQemerge over all scales, suggesting significant market-level reconstructions at these times.

Cancer metastasis is one of the leading causes of death in cancer patients. Dysregulation of the WNT signaling pathway is known to increase the risk of cancer metastasis by leading to excessive accumulation of β-catenin, which activates epithelial–mesenchymal transition (EMT) mechanisms that induce cell motility. Although mono and combination therapies are being developed to prevent metastasis by controlling the abnormally elevated levels of β-catenin, there are limitations in comparing and predicting the treatment effects due to the complexity of cell signaling pathways. In addition, uncertainty exists in determining the optimal combination ratio of each therapy in combination treatments. In this study, we aim to address these challenges by investigating optimal modulation strategies to minimize β-catenin concentration, using a mathematical model that comprehensively describes the interactions between the WNT signaling pathway and transforming growth factor-β (TGF-β) involved in EMT, along with optimal control theory. We analyze the efficacy of monotherapy strategies to prevent the hyperactivation of β-catenin and quantitatively determine the optimal combination ratio for preventing EMT, based on the E-cadherin biomarker as an indicator of EMT. Furthermore, we identify the optimal therapy protocol that minimizes patient burden while maximizing therapeutic efficacy by incorporating considerations of control sequences and delay times. Our findings are expected to not only enhance the understanding of the complex signaling pathways underlying cancer metastasis but also contribute to the development of novel therapeutic approaches.

The Complex Social Internet of Things (CSIoT) integrates the connectivity of IoT with the relational dynamics of complex social networks, creating systems where devices autonomously form and manage relationships. The centrality measures specify the topological characteristics of each node in terms of local and global information of the node in the network. The detection of effective devices in disseminating information across CSIoT networks is critical for optimizing communication, improving network performance, and ensuring efficient resource utilization. In this paper, temporal centrality measures are used to identify influential devices in information dissemination. For this purpose, first, the centrality measures for SIoT network devices have been redefined, and then, using the SIR model, each of the measures has been evaluated in terms of the success rate in identifying effective devices in information dissemination. The results have shown that in SIoT networks that have a high clustering coefficient, the centrality measures of closeness and betweenness have a better performance in identifying influential devices that are effective in spreading information. Also, for networks that have a high degree of heterogeneity, the device coreness centrality and device Katz centrality measures perform better than other measures. Finally, the results show that mobile devices play a more important role in disseminating information than static devices.

This study addresses influence maximization in complex networks, aiming to identify optimal seed nodes for maximal cascades. Greedy methods, though effective, prove inefficient for large-scale social networks. This article introduces a double-chromosome evolutionary algorithm to tackle this challenge efficiently. This method introduces a smart operator for stochastic selection based on the node degree to initialize the primary solutions. A novel smart approach was also employed to improve the convergence of the proposed method by ranking the nodes existing in the current solution and using a blacklist to reduce the probability of selecting the nodes that might be influenced by the selected nodes. Moreover, a novel local search operator with appropriate efficiency was proposed to increase influence. To maintain solution diversity, a population diversity retention operator is integrated. Experimental evaluations on six real-world networks revealed the algorithm’s superiority in terms of influence rates, consistently outperforming the DPSO algorithm and ranking second to CELF with minimal margin according to statistical analysis using the Friedman test. For runtime efficiency, the proposed method demonstrated significantly shorter execution times compared to CELF and DPSO, showcasing its scalability and robustness. These results underscore the method’s effectiveness for applications requiring accurate identification of influential nodes.

In this paper, the fixed-time consensus problem for leader–follower multiagent systems with directed graphs is discussed. First, a new fixed-time state observer of the multiagent system is designed. In the new state observer, an auxiliary matrix is introduced which can be theoretically obtained by solving a linear matrix inequality. The important role of the auxiliary matrix is that it makes the upper bound of the fixed time for the state observation of multiagent system solvable and more accurate, which enable the observation error system converges faster. Based on the new fixed-time state observer, a sufficient condition is given with which the observation of the state of multiagent system can be reached in fixed time. Second, a new fixed-time control protocol is designed for the leader–follower multiagent system. In the new controller, another auxiliary matrix is introduced which can also be theoretically obtained with linear matrix inequality. With the new control protocol, the closed-loop leader–follower multiagent system is theoretically shown that the fixed-time consensus can be reached in fixed time by means of the concept of fixed-time stability, Lyapunov stability theory, and LaSalle’s invariance principle. The important role of the new control protocol is that it also makes the fixed time for the consensus multiagent system solvable and then the closed-loop multiagent system converges faster. Finally, some numerical simulations are presented to demonstrate convincingly the superiority of the method and results obtained in this paper. From the simulations, it can be seen that in comparison with some existing works, the estimate of fixed time for the consensus problem may be more accurate and faster.

This work examines a discrete prey–predator model using the fractional derivative. The conditions for the existence and stability of the fixed points in the model are identified. The analysis is centered on exploring various bifurcations at the positive fixed point to understand their ecological implications. Using bifurcation theory, bifurcations related to period doubling, Neimark–Sacker, and strong resonances are studied. Lastly, the analytical results are confirmed through numerical simulations using the MATLAB package MatContM, and a controller is applied to relieve the extreme instability within the system.

With the development of social media and online platforms, the speed of dissemination and influence of emergencies in cyberspace have increased significantly. The rapid change of public opinion, especially the reversal of public opinion, may have a significant impact on social stability and government credibility. The hypernetwork structure has complex multilevel and multidimensional characteristics, and it is of great significance to analyze the multiple participating subjects of public opinion evolution and their complex relationships based on the hypernetwork theory, and to further identify the public opinion reversal for the public opinion response and guidance of emergencies. According to the complex interaction between the participants of emergencies and internal and external factors, this paper constructs a hypernetwork model including four subnets of users, time series, opinions, and emotions, and analyzes the network structure in detail. On this basis, the method steps of emergency public opinion inversion recognition are proposed. Taking the public opinion event caused by Hongxing Erke donation during the rainstorm in Henan Province of China as an example, the empirical analysis is carried out. The research shows that the proposed emergency hypernetwork model provides effective support for the identification of public opinion inversion, and the identification method of public opinion inversion based on the hypernetwork is helpful to find the trend of public opinion evolution, so as to infer the tendency of public opinion inversion, which provides new ideas for the related research of public opinion monitoring and emergency response.

While modeling students’ learning behavior or preferences has been found to be a crucial indicator for their course achievement, very few studies have considered it in predicting the achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior models constitute a comprehensive student’s learning behavioral pattern which is later used for the prediction of their course achievement. Lastly, using a multiple-criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with high accuracy through modeling their learning behavior during online courses.