Complexity最新文献

We investigate the nonlinear dynamics of a discrete-time predator–prey model governed by a Holling Type-II functional response. Starting from a biologically motivated continuous-time system, we derive its discrete analogue via the explicit Euler method and employ nondimensionalization to reduce the number of parameters. The resulting two-dimensional nonlinear system is analyzed for the existence and local stability of fixed points. Analytical conditions are established for the occurrence of flip (period-doubling) and Neimark–Sacker bifurcations, characterizing the transition from steady states to periodic and quasi-periodic behavior as system parameters vary. Employing center manifold theory and normal form computations, we derive expressions for the first Lyapunov coefficient to determine the direction and stability of bifurcating invariant curves. To suppress chaotic dynamics induced by bifurcations, we implement a hybrid feedback control mechanism and establish sufficient conditions under which the controlled system regains local asymptotic stability. Numerical results, bifurcation diagrams, and phase portraits corroborate the theoretical results. The framework developed herein provides a rigorous foundation for analyzing and stabilizing discrete ecological models with nonlinear interaction terms.

Social media platforms, such as X (formerly Twitter), provide users with concise but impactful tools to express their views and feelings. Users present their views and express their feelings in hashtags and emojis on a wide range of topics. The sheer volume of this textual data offers a rich source for analyzing public sentiment and emotions. Numerous machine learning and deep learning approaches have been presented lately for optimal emotion detection and sentiment analysis of these tweets. Given the complexity of processing human language, natural language processing (NLP) techniques face the challenge of explainability in their decision-making process. To bridge this gap, we introduce an explainable NLP-based framework for the recognition of human emotions within textual data. We propose a novel recurrent neural network architecture incorporating a bidirectional long short-term memory layer for emotion prediction and sentiment analysis on English tweets. The performance of the proposed model is evaluated with real-world X data against benchmark techniques. The proposed model achieves accuracy, precision, recall, and an F1-score of over 90%, which is higher than the considered benchmark models. Subsequently, we integrate the explainable artificial intelligence (XAI) approaches, namely, local interpretable model-agnostic explanations (LIME) and SHapely Additive exPlanation (SHAP) to explain the decision-making process behind the proposed model’s prediction. Applying these XAI techniques not only boosts the proposed model’s transparency but also reinforces its reliability in accurately processing and explaining textual data.

This study employs the feature ranking network method to investigate the foreign exchange (FX) market to uncover the underlying structural transition by observing the dependencies and stability of currencies. For this purpose, the FX market’s time series of 50 currencies is examined from January 2020 to October 2023 against the US dollar, covering the COVID-19 pandemic and the Russia–Ukraine war. Using the random forest regressor, the feature ranking matrix is determined by utilizing the returns of currencies on a given day to predict the feature ranks for the following day. The dependency network is constructed using the threshold method, revealing that the topological properties of the networks undergo significant changes, especially during the war. Asian currencies grab the central positions of the dependency network, indicating their high reliance. We select four representative currencies to provide a clearer and more focused analysis of currency dependency, stability, and entropic trends. It is observed that the war triggers instability in currencies and increases the developing countries’ currency dependence. The global entropy increases with minor fluctuations during the war, and a sharp decline in entropy was observed at the beginning of 2023, indicating an extremely high dependence of the currencies of Russia (RUB), the Philippines (PHP), and Bangladesh (BDT) on others. For comparative analysis, we discuss the topological properties of the EUR-based network alongside those of the USD-referred market. The proposed dependency network–based analytical framework provides valuable and sustainable insights for observing currency resilience and contagion in pandemic and geopolitical events.

The present paper employs topological data analysis methods to reveal ‘holes’ (stable persistent homologies) in the semantic spaces of words, bigrams, and trigrams of the English and Russian languages, and to ascertain their boundaries. Furthermore, the paper selects those holes that belong to the large-scale (coarse-grained) structure of the language that are not just local inhomogeneities of the sample—it appears that there are around a dozen of them for each of the languages (English and Russian). These boundaries delineate ‘blind spots’ of the respective language—the regions of the semantic spaces that do not contain words/bigrams/trigrams of the language—that is, regions of concepts that the language cannot see through its lens. The secondary goal of the paper is to solve the bot-detection problem in its strong statement, that is, one trains the classifiers on one set of bots and tests on the another set of bots. To this end, we estimate the average distances from words, bigrams, and trigrams of a text to the boundaries of the nearest ‘hole’, for texts both written by humans and generated by bots, and construct classifiers. The classifiers show comparatively good results: the average accuracy amounts to 0.8.

This study focuses on overcoming critical human resource challenges within the healthcare sector and exploring the formulation and implementation of measures to reduce the turnover rate of the medical staff. The concept of psychological contract governance posits that hospitals should prioritize fostering positive interpersonal relationships, providing robust social support, and cultivating a supportive work environment. This approach addresses the psychological and spiritual needs of the medical staff beyond mere material incentives, thereby ultimately enhancing workforce stability. Current research on psychological contract among the medical staff remains limited, predominantly focusing on identifying antecedents of turnover behavior and applying intervention strategies from a psychological contract standpoint. Grounding our analysis in the core dimensions of psychological contract, this study employs evolutionary game theory to model the strategic interactions between hospitals that implement psychological contract governance and the turnover decisions of the medical staff, under varying labor market supply and demand conditions. Our analysis elucidates the specific contexts and mechanisms by which psychological contract governance influences turnover decisions. Furthermore, we utilize system simulation to explore key parameters affecting the evolutionary outcomes for both parties involved and propose strategies to improve the retention of the medical staff. It is recommended that psychological contract governance strategies be tailored to current labor market conditions, with particular emphasis on the dynamics of supply and demand. Implementing a systematic incentive framework is advantageous, as it effectively addresses the multifaceted needs of the medical staff, encompassing both material and psychological motivators. In addition, strengthening negative organizational constraints, while maintaining a positive psychological contract governance framework, is essential for optimizing overall outcomes. This research aims to provide valuable insights for human resource management within medical institutions and to offer a theoretical foundation for talent management decisions made by hospital administrators and relevant healthcare regulatory bodies.

This work focuses on the phenomenon of partial migration in time-periodic environments. Time periodicity refers to cyclic variations in environmental conditions, such as seasonal changes, significantly influencing an organism’s habitat and resources. Partial migration, observed in numerous species, including birds, fish, and mammals, involves a fraction of the population undertaking migratory movements while others remain sedentary. We have developed a mathematical framework for understanding evolutionarily stable strategies (ESSs) and ideal free distributions (IFDs) in environments that change periodically over time. By focusing on a range of periodic Beverton–Holt functions, we have established a criterion involving environmental functions that is both necessary and sufficient to determine the existence of ESSs and IFDs. This criterion assesses environmental variations across both spatial and temporal dimensions throughout a periodic cycle, thereby broadening the application of IFDs to encompass general time-periodic contexts. These strategies are evolutionarily stable and act as neighborhood invaders within the framework of evolutionary game theory. Our results build upon previous work that primarily considered temporally constant environments. Using a stage-structured time periodic matrix model, we show the existence and stability of the k-cycle. In this study, we demonstrated the existence of ESS and IFD through a series of numerical examples, which supports the theoretical findings.

Without a vaccination solution, implementing intermediary defense measures such as mask wearing becomes imperative to curtail disease transmission, hinging on individuals’ choices to wear masks. Conversely, postinfection treatment serves as a last-resort avenue for disease reduction. This model proposes an innovative epidemic modeling approach to address these dual aspects, integrating mask-wearing behavior and treatment decisions as strategic choices grounded in game theory principles. The primary objective of this model is to delve into the intricate interplay between individual behaviors and their implications for disease propagation, particularly in the absence of vaccination. By factoring in rational decisions made by agents within a dynamic epidemic context, the model seeks to unravel the intricate connections between adopting masks and seeking treatments and their subsequent impact on disease control. By incorporating mask adoption and treatment seeking as dynamic variables, this model sheds light on the efficacy of preventive measures and treatment protocols in managing epidemic outbreaks. The model investigates the transition rates from susceptibility to mask adoption and infection to treatment seeking through a comprehensive evolutionary game theory lens. The inherent strategies related to mask wearing and treatment are depicted using an extensive evolutionary game theory framework among societal individuals, presented through an illustrative phase diagram. In-depth numerical simulations indicate that the efficacy of masks and treatment could implicitly reduce community infection risks, particularly when these solutions are reliable and cost-effective. This entails exploring how the evolution and coexistence of mask wearing and treatment strategies interact, using metrics such as the social dilemma’s impact and the count of individuals benefiting from these approaches.

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.