Complexity最新文献

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.

Bidirectional encoder representation from transformer (BERT) models are increasingly being employed in the development of natural language processing (NLP) systems, predominantly for English and other European languages. However, because of the complexity of the language’s morphology and the scarcity of models and resources, the BERT model is not widely employed for Amharic text processing and other NLP applications. This paper describes the fine-tuning of a pretrained BERT model to classify Amharic news documents into different news labels. We modified and retrained the model using a custom news document dataset separated into seven key categories. We utilized 2181 distinct Amharic news articles, each comprising a title, a summary lead, and a comprehensive main body. An experiment was carried out to assess the performance of the fine-tuned BERT model, which achieved 88% accuracy, 88% precision, 87.61% recall, and 87.59% F1-score, respectively. In addition, we evaluated our fine-tuned model against baseline models such as bag-of-words with MLP, Word2Vec with MLP, and fastText classifier utilizing the identical dataset and preprocessing module. Our model outperformed these baselines by 6.3%, 14%, and 8% in terms of accuracy, respectively. In conclusion, our refined BERT model has demonstrated encouraging outcomes in the categorization of Amharic news documents, surpassing conventional methods. Future research could explore further fine-tuning techniques and larger datasets to enhance performance.

This article presents a method of constructing counter-examples and a complete counter-example to the linear programming model alleged to be the solution to the traveling salesman problem. The counter-example is checked against the model proposed by Diaby et al. However, it applies to all similar formulations of the TSP problem.

Although the model in question was published in 2006, and there were several discussions regarding its correctness, the counter-example was never presented.

The presented counter-example is a regular graph, and the aim was not to have an example with the least possible size; therefore, the focus was on clarity. The counter-example has, therefore, 366 nodes in two main clusters, each node (in the main part) having exactly four connections to other nodes in the cluster.