Complexity最新文献

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.

R&D partner diversity (RPD) is crucial for enhancing a firm’s innovation level, with innovation quality being a pivotal indicator of the firm’s overall capacity for innovation. However, the relationship between RPD and innovation quality has received little attention in the literature. This paper aims to unravel the influence of RPD on innovation quality and the underlying mechanisms. We conduct empirical research utilizing data from 463 publicly listed Chinese manufacturing companies to achieve this goal. Using a negative binomial model for data analysis, we find that RPD has a positive impact on the quality of innovation. The micromechanism analysis reveals that both exploratory learning and exploitative learning play a mediating role in the relationship between RPD and innovation quality. Furthermore, we discover that the strength of the relationship between RPD and innovation quality varies depending on the types of R&D partners and corporate ownership. Specifically, firms that collaborate with universities, competitors, users, or research institutes strengthen the positive effect, whereas forming alliances with other entities within the same group mitigates it. RPD has a more significant positive influence on the quality of innovation in non-state-owned enterprises compared to state-owned enterprises. These findings are robust to a battery of sensitivity tests, which provide valuable insights for firms seeking to enhance their innovation quality by fostering diverse R&D partnerships.

Optimal control theory and evolutionary game theory are essential tools for comprehending and influencing the intricate behaviors of complex systems, particularly in the context of disease transmission and strategies for intervention. In this study, we leverage optimal control theory to address short-term disease dynamics using a single season strategy. In contrast, evolutionary game theory guides our approach on a longer timescale through a repeated seasonal model. We employ a system of nonlinear ordinary differential equations to dissect how the dynamics of primary infections impact the spread of Nipah disease. Our novel dynamic system extends the classical susceptible-infected-recovered (SIR) model by introducing four distinct population categories: humans, bats, fruit, and animals. We delve into this epidemic model’s theoretical underpinnings, examining disease-free and endemic equilibria to establish stability conditions. To address the challenge of optimally reducing the number of infectious individuals, we formulate an optimal control problem featuring four distinct control strategies. These strategies are deployed to mitigate disease transmission, all driven by a generalized incidence function. By identifying the optimal amalgamation of these strategies, we aim to minimize the infectious population. Decisions about the selection and execution of diverse disease control policies rest upon theoretical projections and numerical simulations conducted over a single season. Our study also incorporates evolutionary game dynamics, wherein individuals choose whether to adopt awareness and protection measures after the disease has circulated within the community. We meticulously explore the impact of such awareness and protection measures to underscore their significance within the context of the epidemic model across multiple time steps. Moreover, we systematically analyze the parameter properties within the epidemic model to address diverse real-world scenarios.

Modelling has become an eminent tool in the study of ecological systems. Ecological modelling can help implement sustainable development, mathematical models, and system analysis that explain how ecological processes can promote the sustainable management of resources. In this paper, we also chose a four-dimensional discrete-time Lotka–Volterra ecological model and analyzed its dynamic behavior. In particular, we derived the parametric conditions for the existence of biologically feasible solutions and the stability of the fixed points. We also provided graphs to study the spectrum behavior of all fixed points. In addition, we have seen that when the intrinsic dynamics of the population exceed a certain threshold, the system bifurcates. This particular range of inherent population dynamics depends on the values of other biological parameters and the initial population. We proved that the instability of the model resulted in Neimark–Sacker and period-doubling bifurcations. To confirm these two types of bifurcation, we used bifurcation theory, and to find the direction of bifurcation, we used graphical results. Mainly, through novel periodic plots, we confirm the coexistence of the population and the possible equilibrium states. We apply Marotto’s theorem to verify the existence of chaos in the system. To control the chaos, we use a hybrid control feedback methodology. Finally, we provide numerical examples to illustrate our theoretical results. The outcomes of the numerical simulations show chaotic long-term behavior across an extensive range of parameters.

Trajectory planning is a new research topic in the field of automated vehicles (AVs). It is the process of identifying a trajectory for the vehicle to traverse its environment without obstacle collision. Trajectories are computed fast in real time as the environment constantly changes with time. To address these problems, the Ridge Regressive Data Preprocessed Quantum Deep Belief Neural Network (RRDPQDBNN) model is developed. The RRDPQDBNN model intends to carry out effective trajectory planning in autonomous vehicles through enhanced accuracy and minimum time complexity. Initially, in the RRDPQDBNN model, vehicle data are extracted and transmitted to the input layer. Secondly, Ridge Regressive Data Preprocessing is performed to eliminate noisy data from collected vehicle data. Finally, quantum data clustering is carried out in the RRDPQDBNN model to identify the severity of the risk without collision during the trajectory. This, in turn, is effective trajectory planning performed in autonomous vehicles. Experimental results are computed in terms of clustering accuracy, clustering time, error rate, precision, and recall. From experimental results, the RRDPQDBNN model increases clustering accuracy by 11%, precision by 13%, and recall by 5%, as well as reduces clustering time by 31% and error rate by 58% compared to existing methods.