Complexity最新文献

H. Man, M. Wan, Y. Shi, and P. Chen, “Parsing Chinese with Combinatory Categorial Grammar: A Linguistic and Computational Study,” Complexity 2022 (2022): 4057360, https://doi.org/10.1155/2022/4057360.

In the article, there are errors in examples 10–12 and 14–16, which were introduced during the production process. The correct examples are listed below:

(10)

去北极    是  我de梦想

go to the Arctic is  I de dream

‘To go to the Arctic is my dream’

(11)

去北极    de 人   都 很勇敢

go to the Arctic de people all very brave

‘All those who go to the Arctic are brave’

(12)

Q: Where will you go?

A: 去北极。

‘To go to the Arctic’

(14)

台上   坐着  主席团

stage-on  sit-prog presidium

‘on the stage, there sit the presidium’

(15)

一锅饭   吃  十个人

One-CL-rice eat  ten-CL-people

‘A pot of rice can feed ten people’

(16)

不下雨  已经  三个月了

not-rain  already three-CL-month-perf

‘It hasn’t rained for three months’

We apologize for these errors.

The present study investigated the dynamics of the nonlinear stochastic pine wilt epidemic model. An extension of the stochastic to deterministic model is presented. Equilibria, positivity, boundedness, extinction, and disease persistence were studied rigorously. Standard and nonstandard numerical techniques like Euler–Maruyama, stochastic Euler, stochastic Runge–Kutta, and stochastic nonstandard finite difference are presented for computational analysis. Furthermore, the nonstandard method is a dynamically consistent approximation of stochastic differential equations of the pine wilt epidemic model that is efficient, low cost, and independent of time-step size. The comparison section with standard methods strengthens the nonstandard method and supports the theoretical results of the model.

This study proposes a single-season dual-layer susceptible–infectious–recovered–unaware–aware–Vaccinated (SIR-UAV) model that captures the simultaneous dynamics of disease transmission, individual decision-making, and awareness diffusion within a single time scale—distinct from previously published models that consider repeated or seasonal outbreaks. Individuals are categorized as either unaware or aware, where aware individuals possess information that reduces their susceptibility, while unaware individuals remain fully vulnerable. Awareness spreads through physical encounters (e.g., interaction with infected or aware individuals) and virtual sources such as social media, television, and newspapers. Both aware and unaware individuals can receive vaccination, acquiring strong immunity. The model includes susceptible, infected, recovered, and vaccinated compartments in a positively bounded and well-mixed population, with further stratification by awareness status. By modeling the interactions between these groups, the framework evaluates the impact of awareness campaigns and vaccination strategies on mitigating disease spread during a single epidemic season. The model also incorporates a vaccination game using evolutionary game theory (EGT), where the vaccination rate is influenced by the cost of vaccination and the number of infected individuals. We depicted the influence of social policies on the final epidemic size (FES), vaccine coverage (VC), and aware susceptible individuals (ASI). Mathematical analysis shows that effective awareness reduces the spreading of infection and increases VC. Effective vaccine removes the disease from society within an early period for affordable vaccine costs. Surprisingly, this study also shows more vaccinations increase infections due to lack of sufficient efficiency. This study also shows the increased awareness rate increases infection within the aware population due to false or ineffective awareness. Again, despite the vaccine cost increases, the infected number reduces. Due to effective awareness, people focus on awareness, avoiding vaccinations.

COVID-19, although now at the endemic stage in numerous regions, is a serious public health challenge. While the virus spreads primarily through direct human-to-human contact, it can also be transmitted indirectly through contaminated environments. In this study, we propose a nonlinear epidemic model to analyze the first three waves of COVID-19 in Nigeria, Guinea, and Sierra Leone. Through the maximum likelihood method, we derive estimates for various parameters, including the direct transmission rate, indirect transmission rate, and initial infection rate. Our studies investigate changes in both direct and indirect transmission in the early waves of COVID-19 across West Africa, placing the virus’s behavior in perspective in several outbreaks. We also draw attention to the importance of consideration of structurally and practically identifiable combinations of parameters in producing realizations with biological significance. The results demonstrated significant variations in direct transmission rates, notably in Nigeria, whereas indirect transmission rates were generally steady across every country. It should be noted that direct transmission is higher compared with indirect transmission due to increased social interaction and mobility within the pandemic area. The low environmental survival of the virus is also a contributory factor to the limiting effect observed with indirect transmission. These pieces of evidence point to public health interventions that must focus on the prevention of direct transmission, such as the promotion of social distancing and mask use, with consideration for environmental factors. Our results emphasize the need for multiple control strategies to be considered for any future pandemic management in the region.

For the credit risk problem, an artificial intelligence credit risk assessment model based on multilayer perceptron (MLP) hybrid clustering is proposed. This study is the first to evaluate credit risk evaluation indexes based on a combination of natural language processing (NLP), cluster analysis, and correlation analysis and to construct a credit risk evaluation index system. The weights of the indicators are then determined through the application of the analytic hierarchy process (AHP) and the criteria importance through intercriteria correlation (CRITIC) assignment method; then, the weighted credit evaluation indicator data are used to construct a credit risk assessment model based on the MLP to predict the credit status of the business entity, and then a Gaussian mixture model (GMM) combined with the expectation–maximization (EM) algorithm is employed to divide the credit risk levels into intervals. Finally, based on the research information of 246 family farms and ranches in 12 cities in the Inner Mongolia region, an empirical analysis is conducted. The results demonstrate that (1) most of the investigated samples of family farms in Inner Mongolia are in a low-risk state, which follows the law of large numbers and practical significance. (2) The accuracy of the regression model to predict the credit risk of family farms in Inner Mongolia is examined with the ROC curve, and the results show that the AUC value is 0.92, and the MLP model fits well.

Research for decades has concentrated on graphs of algebraic structures, which integrate algebra and combinatorics in an innovative way. The goal of this study is to characterize specific aspects of bipartite and inverse graphs that are associated with specific algebraic structures, such as weak inverse property quasigroups and their isotopes, commutator subloops, associator subloops, and nuclei, with a focus on structural and topological characteristics. This research aims to highlight the link between mathematical algebraic systems and graph-theoretic capabilities, paving the path for theoretical advances and applications in computer science through Simulink. The methodology blends algebraic techniques based on quasigroup structural components with basic ideas of simple graphs via edge labeling. Furthermore, mathematical methods are used for property analysis, graph visualization, and construction. The analysis shows that inverse and bipartite graphs with weak inverse property loops have distinct structural patterns, such as supporting substructures of specific properties, connectedness, and symmetry in the vertex system. Finally, our findings lay the groundwork for future detection of more complex algebraic structures and dynamic graph models, as well as various opportunities for both theoretical research and practical application.

Z. Zhang, J. Du, Q. Meng, X. Rong, and X. Fan, “The Spread of Information in Virtual Communities,” Complexity 2020 (2020): 6629318, https://doi.org/10.1155/2020/6629318.

We apologize for this error.

As a common social phenomenon, group imitation behavior holds significant research value in the fields of biological group collaboration and artificial swarm intelligence. This paper constructs a behavior imitation model integrating information dissemination mechanisms based on the theory of multiagent systems. The model aims to reveal the influence mechanism of group dynamic characteristics and information interaction intensity on the consistency of group behavior. The model architecture consists of two parts. The first part is an information dissemination model improved upon the SIR model, which introduces a perception radius to analyze how neighboring interactions affect the information diffusion rate. The second part is a multiagent group aggregation model based on social mechanics, enabling individuals to form groups through parameters like attraction, repulsion, speed, and movement direction. Groups spread aggregation and imitation information through interactions with neighboring individuals. Then, based on the breadth of the information they receive, they imitate exemplary groups through intergroup imitation effects. Through complex system simulations, the experimental results show that the consistency of group imitation behavior is positively correlated with the perception radius of individuals. This research provides a new modeling framework and analytical perspective for understanding the emergence mechanism of swarm intelligence.

The primary objective of this research paper is to conduct a bibliometric analysis of the prevailing research landscape pertaining to agent-based modeling (ABM). This analysis encompasses an examination of key contributors, affiliated academic institutions, influential publications, and prominent journals within the domain. To achieve this, a dataset consisting of 11,477 scholarly papers retrieved from the ISI Web of Science database has been curated, using keywords specifically related to ABM, spanning the period from 1996 to 2024. Employing n-gram analysis techniques on titles, keywords, abstracts, and keyword-plus fields has unearthed a multitude of domains wherein ABM has been applied with notable success. Our findings, as delineated in this paper, underscore a sustained and robust growth in scholarly interest in the realm of ABM during the specified temporal span, characterized by an impressive annual growth rate of 25.29%. Furthermore, our study contributes to the identification and analysis of salient keywords and emerging trends, thereby elucidating key research trajectories within this domain. The identification of collaborative networks among authors, their respective academic affiliations, and the geographical distribution across various countries and territories offers valuable insights into the global proliferation of ABM as a research methodology. The findings offer valuable insights into the widespread applications of ABM across various domains, including climate change, social networks, supply chain dynamics, public health studies, financial market analysis, and population dynamics. The results of the study can help in guiding future research and practical applications of ABM in these and other multifaceted areas.

Higher-order networks (HON) provide a suitable frame to model connections that involve groups of nodes—representing interacting individuals or other types of agents—of different sizes. They allow us to take into account not only pairwise interactions but also connections binding three or four or any other natural number of nodes together. Motivated by the consideration that the existence of higher-order interactions may impact, among others, the process of diffusion of new products, the spreading of ideas, and the adoption of practices, we propose and study here a version of the celebrated Bass model on top of HON. We define a mean-field equation that contains terms up to the order at which interactions might make a significant contribution. The impact of the paper is twofold. By considering and comparing different maximal orders of interaction and analyzing how they influence certain times that are important in the diffusion process, we show that HON indeed has an impact and yields a greater accuracy in modeling results. The second contribution of the paper, also of interest for future works, consists of a novel procedure we develop for the construction of HON with assigned generalized mean degrees. We also show that the behavior of the take-off time with the size of the orders contribution undergoes a phase transition where the link density of the network and the related higher-order structures act as the characterizing condition for one phase or the other.