Complexity最新文献

Normal and aberrant cognitive functions are the result of the dynamic interplay between large-scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for neurodegenerative disease evolution. Fusing modern dynamic graph network theory techniques and control theory applied on complex brain networks creates a new framework for neurodegenerative disease research by determining disease evolution at the subject level, facilitating a predictive treatment response and revealing key mechanisms responsible for disease alterations. It has been shown that two types of controllability—the average and the modal controllability—are relevant for the mechanistic explanation of how the brain navigates between cognitive states. The average controllability favors highly connected areas which move the brain to easily reachable states, while the modal controllability favors weakly connected areas representative for difficult-to-reach states. We propose two different techniques to achieve these two types of controllability: a centrality measure based on a sensitivity analysis of the Laplacian matrix is employed to determine the average controllability, while graph distances form the basis of the modal controllability. The concepts of “choosing the best driver set” and “graph distances” are applied to measure the average controllability and the modal controllability, respectively. Based on these new techniques, we obtain important disease descriptors that visualize alterations in the disease trajectory by revealing densely connected hubs or sparser areas. Our results suggest that these two techniques can accurately describe the different node roles in controlling trajectories of brain networks.

We study the existence of fixed points, local stability analysis, bifurcation sets at fixed points, codimension-one and codimension-two bifurcation analysis, and chaos control in a predator-prey model with Holling types I and III functional responses. It is proven that the model has a trivial equilibrium point for all involved parameters but interior and semitrivial equilibrium solutions under certain model parameter conditions. Furthermore, local stability at trivial, semitrivial, and interior equilibria using the theory of linear stability is investigated. We have also explored the bifurcation sets for trivial, semitrivial, and interior equilibria and proved that flip bifurcation occurs at semitrivial equilibrium. Furthermore, it is also proven that Neimark–Sacker bifurcation as well as flip bifurcation occurs at an interior equilibrium solution, and in addition, at the same equilibrium solution, we also studied codimension-two 1 : 2 strong resonance bifurcation. Then, OGY and hybrid control strategies are employed to manage chaos in the model under study, which arises from Neimark–Sacker and flip bifurcations, respectively. We have also examined the preservation of the positive solution of the understudied model. Finally, numerical simulations are given to verify the theoretical results.

This paper studies the complexity of the decision system when a brand enterprise introduces new products and makes dynamic quality decisions to cope with imitation threats caused by a counterfeit enterprise. Using game theory, nonlinear system theory, and numerical simulation, the complexity characteristics of this system and consumer utility are further discussed; moreover, delay feedback control is used to restrain chaos. The conclusions are as follows: (1) The stable scope of the brand enterprise price adjustment enlarges with the increase of the adjustment parameters of new product quality and imitation price. (2) The attraction domain of the initial decisions of the counterfeit enterprise decreases when the brand enterprise makes dynamic decisions on new product quality; a higher new product quality adjustment parameter can reduce the imitation ability of the counterfeit enterprise. (3) With the growth of the degree of substitution between new products and existing products, the quality of new products is improved, the output of existing products is reduced, and the counterfeit enterprise withdraws from the market if the degree of substitution between existing products and imitations is high. (4) The dynamic system can suppress chaos and restore stability by using the delay feedback control method. Results are of great importance for managers to make reasonable decisions when dealing with imitation threats.

Infectious diseases pose a significant threat to global health, necessitating the development of effective vaccination strategies. This study examines the dynamics of viral infections and immune responses, with a particular focus on the roles of antibodies and CD8+ T cells induced by vaccination. Through a mathematical model, we explore the intricate interactions between host cells and viruses to assess the impact of vaccination on viral replication. Our findings align with experimental results, demonstrating that vaccination substantially enhances immune responses and reduces viral replication. The contributions of both antibody and CD8+ T cell responses are shown to be vital for achieving optimal vaccine efficacy. The model’s predictions, validated against experimental observations, emphasize the need to incorporate mechanisms that induce robust immune responses in vaccine design. This study underscores the critical role of mathematical modeling in understanding immune dynamics and in refining vaccination strategies to develop more effective treatments against viral infections.

Network analysis involves using graph theory to understand networks. This knowledge is valuable across various disciplines like marketing, management, epidemiology, homeland security, and psychology. An essential task within network analysis is deciphering the structure of complex networks including technological, informational, biological, and social networks. Understanding this structure is crucial for comprehending network performance and organization, shedding light on their underlying structure and potential functions. Community structure detection aims to identify clusters of nodes with high internal link density and low external link density. While there has been extensive research on community structure detection in single-layer networks, the development of methods for detecting community structure in multilayer networks is still in its nascent stages. In this paper, a new method, namely, IGA-MCD, has been proposed to tackle the problem of community structure detection in multiplex networks. IGA-MCD consists of two general phases: flattening and community structure detection. In the flattening phase, the input multiplex network is converted to a weighted monoplex network. In the community structure detection phase, the community structure of the resulting weighted monoplex network is determined using the Improved Genetic Algorithm (IGA). The main aspects that differentiate IGA from other algorithms presented in the literature are as follows: (a) instead of randomly generating the initial population, it is smartly generated using the concept of diffusion. This makes the algorithm converge faster. (b) A dedicated local search is employed at the end of each cycle of the algorithm. This causes the algorithm to come up with better new solutions around the currently found solutions. (c) In the algorithm process, chaotic numbers are used instead of random numbers. This ensures that the diversity of the population is preserved, and the algorithm does not get stuck in the local optimum. Experiments on the various benchmark networks indicate that IGA-MCD outperforms state-of-the-art algorithms.

This work explores the complicated realm of fullerene structures by utilizing an innovative algebraic lens to unravel their chemical intricacies. We reveal a more profound comprehension of the structural subtleties of fullerenes by the computation of modified polynomials that are customized to their distinct geometric and electrical characteristics. In addition to enhancing the theoretical underpinnings, the interaction between algebraic characteristics and fullerene structures creates opportunities for real-world applications in materials science and nanotechnology. Our results provide a novel viewpoint that bridges the gap between algebraic abstraction and chemical reality. They also open up new avenues for the manipulation and construction of materials based on fullerenes with customized features. Topological or numerical descriptors are used to associate important physicomolecular restrictions with important molecular structural features such as periodicity, melting and boiling points, and heat content for various 2 and 3D molecular preparation graphs or networking. The degree of an atom in a molecular network or molecular structure is utilized in this study to calculate the degree of atom-based numerics. The modified polynomial technique is a more recent way of assessing molecular systems and geometries in chemoinformatics. It emphasizes the polynomial nature of molecular features and gives numerics in algebraic expression. Particularly in this context, we describe multiple cages topologically based on the fullerene molecular form as polynomials, and several algebraic properties, including the Randić number and the modified polynomials of the first and second Zagreb numbers, are measured. By applying algebraic methods, we computed topological descriptors such as the Randić number and Zagreb indices. Our qualitative analysis shows that these descriptors significantly improve the prediction of molecular behavior. For instance, the Randić index provided insights into the stability and reactivity of fullerene structures, while the Zagreb indices helped us understand their potential in electronic applications. Our results suggest that modified polynomials not only offer a refined perspective on fullerene structures but also enable the design of materials with tailored properties. This study highlights the potential for these algebraic tools to bridge the gap between theoretical models and practical applications in nanotechnology and materials science, paving the way for innovations in drug delivery, electronic devices, and catalysis.

High risk of braking easily causes more safety accidents. In this paper, the driving experiments on G350 Gengda-Yingxiu section containing continuous downhill tunnel group. Three-axle trucks under standard load and overload conditions were considered. The research proposes a method for ensuring safe truck driving on continuous downhill sections of mountain roads based on the rise in brake drum temperature. The study collects data on brake drum temperature and braking duration from an experimental vehicle under the coupling action of human-vehicle-road-environment. Through comparative analysis, theoretical derivation, and model construction, conclusions are drawn. The results indicate that the rise in brake drum temperature is influenced by the factors such as overload, alignment, road slope, and sections with bright and dark lines. The initial brake drum temperature, operating speed, and total vehicle mass are identified as the main controlling factors for the change in brake drum temperature. The study also demonstrates that water drenching can significantly reduce the rate of brake drum temperature rise, thereby ensuring driving safety. Furthermore, a model is constructed based on the relationship between brake drum temperature rise and various factors, which allows for the calculation of the corresponding safe slope length and average slope gradient. This model can be used for evaluating or designing overall load requirements. The research on brake drum temperature rise characteristics and braking behaviour under drenching conditions provides effective support for route design, traffic management, and the establishment of safety service facilities.

In this article, the fixed-time stabilization issue is investigated for a kind of general p-norm stochastic nonlinear systems. The feature of the considered systems is that all powers are any positive rational numbers, which means this type of systems includes many previously considered systems. Firstly, a higher accurate upper bounded estimation of settling time is given by applying an ingenious variable transformation and the definition of Gamma function, thereby obtaining an improved stochastic fixed-time stability theorem. Then a continuous state-feedback controller is designed for the general p-norm stochastic systems by using the adding a power integrator method, and the designed controller is proved to ensure the fixed-time stability of the considered systems in light of stochastic fixed-time stability theorem. Finally, numerical simulation results of fixed-time stabilizer and finite-time stabilizer indicate the effectiveness of the developed scheme.

To evaluate the effectiveness of certain complexity features extracted from CT images of the liver for predicting the survival of patients with hepatocellular carcinoma, either exclusively or in conjunction with specific diagnostic indicators, we gathered data from presurgery CT scans of 103 HCC patients with survival period either above (n = 65) or below (n = 38) 42 months after hepatectomy. The two-dimensional Hilbert curve was used to maintain both local and global structural information to calculate the lattice complexity features. In addition, gray-level co-occurrence matrix features and local binary features were incorporated. These features were assessed for performance of support vector machine predictive models through the receiver operator characteristic curve and area under the curve. The top proficiency was achieved by the lattice complexity features resulting in models with an accuracy of 76.47% and an area under the receiver operator characteristic curve of 0.75. The study found that two-dimensional lattice complexity features derived from CT images that covered the entire abdomen have the potential to predict survival patients with in hepatocellular carcinoma using support vector machine models.