Complexity最新文献

This study introduces a novel hybrid deep learning framework to enhance hotspot detection in photonic crystal design, addressing challenges in accuracy and overfitting. By integrating Capsule Networks (CapsNet) and transformer architectures with ensemble learning and data augmentation, the proposed approach optimizes the identification of wavelength propagation discrepancies in photonic structures. Experiments conducted on the ICCAD-2012 benchmark dataset demonstrate that the ensemble model achieves 90% test accuracy, outperforming standalone CapsNet and transformer models while reducing overfitting. The model also demonstrates strong classification consistency, with an F1-score of 90% and a G-mean of 90%, indicating robust performance across precision–recall balance and class-wise sensitivity–specificity harmony. The framework’s success in balancing performance and generalization highlights its potential to streamline photonic device design for applications in sensing, telecommunications, and energy harvesting. This work bridges advanced machine learning techniques with photonic engineering, offering a scalable and efficient solution for complex light-matter interaction analysis.

In recent years, the study of finite-time stability (FTS) and finite-time control (FTC) of time-delay systems has attracted significant attention from researchers. This article investigates the problems of FTS and FTC for nonlinear systems in the presence of state-dependent delays and parametric uncertainties. The considered delay is time-varying, and the nonlinear system is assumed to satisfy the Lipschitz condition. First, sufficient conditions for ensuring FTS of the nonlinear time-delay system with parametric uncertainties are derived in the framework of linear matrix inequalities (LMIs). Next, LMI-based sufficient conditions are established for guaranteeing FTC via modified state-feedback control. The obtained FTS and FTC conditions are delay-dependent, providing a more precise characterization of the system’s transient behavior. To establish the theoretical results, the Newton–Leibniz formula and a Lyapunov–Krasovskii functional (LKF) candidate were employed. Finally, the effectiveness of the proposed approach is demonstrated through two illustrative examples and corresponding MATLAB simulations.

In recent years, public health crises have impeded economic development and exerted significant shocks on capital markets, particularly affecting investor confidence. Although numerous scholars have examined economic stability during public health crises from various perspectives, few have investigated the stability and recovery of capital markets from the standpoint of investor sentiment. In light of this gap, this study employs the Double Deep Q-Network (Double DQN) model within a multifactor pricing framework to explore how investor sentiment influences stock return predictions and portfolio optimization during public health crises. Using data from China’s A-share market during the COVID-19 pandemic, we construct and incorporate several sentiment indices as key indicators of investor sentiment, including the Baidu Sentiment Index (BD), Douyin Sentiment Index (DY), Toutiao Sentiment Index (TT), Stock Market Investor Sentiment Index (CICS), and the Investor Confidence Index (ICI). The experimental results reveal that incorporating investor sentiment indices significantly enhances the predictive performance of the Double DQN model for stock returns and effectively optimizes the Sharpe ratio of investment portfolios. Among these sentiment indices, the BD index exhibits the highest importance, whereas the ICI index shows the lowest. Moreover, the sentiment indices demonstrate a more pronounced effect in optimizing long-short portfolios compared to long-only portfolios, suggesting that market sentiment plays a crucial role in amplifying irrational market fluctuations during public health crises. These findings underscore the need for governments, investment institutions, and individual investors to recognize the impact of investor sentiment on market volatility to prevent domino effects that could escalate into systemic financial risks. This study provides both theoretical insights and practical implications for investment return forecasting and risk management under such conditions.

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.