Complexity最新文献

This study introduces a novel fractional age-structured Susceptibles-Exposed-Infective-Hospitalized-Recovered-Adults (SEIHRA) model, designed to analyze measles transmission dynamics, particularly in younger populations. By incorporating age structure and an innovative inverse method, the model bridges mathematical rigor with empirical data. We examine equilibrium points, stability, and the basic reproduction number (R₀), while using the inverse method to estimate the time-dependent transmission rate β(t) from real-world outbreak data. Validated with Chinese measles data (1974–2022), the model captures temporal and age-specific trends, achieving an optimal fractional order of 0.94. Sensitivity analysis via the partial rank correlation coefficient (PRCC) technique highlights key parameters influencing R₀. Combining age structure and inverse methods, this work reveals age-specific transmission patterns and evaluates targeted vaccination strategies, offering critical insights for public health policies and global measles eradication efforts.

This work explores the challenges related to the 4-part harmony problem, addressing both the computational complexity of the search space and the benefits of integrating human teaching/learning processes into evolutionary problem-solving approaches. From a computational perspective, we analyze strategies to enhance algorithm efficiency, including parallelization, precomputation of fitness values, directed mutation, and adaptive directed mutation, which collectively reduce the time required to find solutions. Synthetic harmonic models are employed to validate these techniques. Complementing this, we investigate the role of human expertise, emphasizing the synergy between expert teaching and the learning processes of novice students. By examining how human teaching and learning paradigms can inspire innovative problem-solving techniques, we draw on the concept of evolutionary machine teaching, which reduces the search space, applied here to a standard harmonic model. Our findings highlight the potential of integrating computational advancements with methodologies driven by human learning. Specifically, the search space produced by Sharpmony students accounts for less than 1% of the total space. Using this approach, we have achieved a fourfold speedup over previous results of the same quality. Moreover, longer runs of the new approach have provided solutions with an average fitness of less than 1 error, considering the complete set of 50 rules and exceptions.

Cellular automata are powerful tools for simulating dynamic environments. Their ability to model complex systems where the environment actively influences outcomes makes them invaluable for studying phenomena such as wildfires, marine pollution, and population dynamics. However, traditional cellular automata are limited by discrete representations and rigid data structures, hindering their application in spatially complex scenarios. This paper introduces a generalized cellular automaton designed to overcome these challenges. By incorporating continuous space evolution and leveraging tensorial data structures, our model offers a more accurate, flexible, and computationally efficient framework for simulating real-world systems. This approach significantly simplifies the integration of geographical information into discrete simulations, expanding the potential of cellular automata in fields such as environmental science, population ecology, or theoretical physics. Moreover, our work contributes to a deeper understanding of tensorial representations and the concept of time using a computational approach.

RETRACTION: D. Yang, X. Ye, and B. Guo, “Application of Multitask Joint Sparse Representation Algorithm in Chinese Painting Image Classification,” Complexity, 2021, 5546338, https://doi.org/10.1155/2021/5546338.

The authors agree to the retraction.

Urban travel behavior in developing cities forms a complex system with nonlinear interactions among socioeconomic factors, land use patterns, and transportation infrastructure. This study examines these intricate dynamics in Rajshahi City Corporation (RCC), Bangladesh, using a multimodel approach to capture emergent properties of urban mobility. Analyzing data from 2286 households across six zones, we developed three interconnected models: Trip Production Model (TPM), Trip Attraction Model (TAM), and Household Kilometers Traveled Model (HKTM). The TPM showed that increasing household size by one unit boosts trip production by 1.537 times, while a one-unit increase in accessibility raises it by 1.930 times. Interestingly, the TAM revealed that higher accessibility can decrease trip attractions (coefficient: −1.412), indicating emergent congestion effects. The HKTM indicated that a one-unit improvement in road connectivity leads to an increase of 2.652 km in household travel. Our results demonstrate that socioeconomic and land use factors explain 75.1% of the variability in trip production, emphasizing the system’s complexity. The City Center, with the highest entropy index (0.80), attracted the most trips, whereas the Northern Fringe, despite a low entropy (0.52), generated the highest number of trips. These surprising findings highlight the nonlinear relationships in urban mobility and stress the importance of context-specific solutions to address urban transportation challenges. By applying complex systems theory, including concepts of self-organization and feedback loops, we provide a comprehensive framework for understanding and modeling urban transport dynamics in developing areas, offering valuable insights for adaptive policy-making amid rapid urban growth.

RETRACTION: C. Di, J. Peng, Y. Di, and S. Wu, “3D Face Modeling Algorithm for Film and Television Animation Based on Lightweight Convolutional Neural Network,” Complexity 2021, no. 1 (2021): 6752120, https://doi.org/10.1155/2021/6752120.

The above article, published online on 25 May 2021 in Wiley Online Library (https://wileyonlinelibrary.com), has been retracted by John Wiley & Sons Ltd.

The presence of these indicators undermines our confidence in the integrity of the article’s content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process.

Real parameter single objective optimization has been the subject of extensive research. Differential evolution (DE) has exhibited remarkable performance. Recently, long-term search has emerged as a new focal point of real parameter single objective optimization. In existing DE variants for long-term search, integration of multiple mutation strategies or execution of local search is studied. In this paper, an algorithm named DE with allocation of mutation strategy to individual based on fitness ranking (AMSIFRDE) is proposed. In AMSIFRDE, the two aspects are both considered and enhanced. Different individuals are allocated to different mutation strategies, respectively, according to their ranking. In addition, a local search technique processes the median individual and the best one in turn in different generations. Experiments are conducted using the CEC 2020 and 2022 benchmark test suites and demonstrate that AMSIFRDE performs either better than or at least comparably to seven other algorithms for long-term search.

T. Modis, “Complexity in the Wake of Artificial Intelligence,” Complexity 2025 (2025): 7656280, https://doi.org/10.1155/cplx/7656280.

In the article titled “Complexity in the Wake of Artificial Intelligence,” there is an error in Figure 4, where the graph in Figure 4 is identical to the graph in Figure 3. This is incorrect. The corrected figure is shown below and is listed as Figure 1:

We apologize for this error.

In this article, we propose an aquaculture model with impulsive harvesting predator and density-dependent nonlinear releasing prey. By taking advantage of the stroboscopic map and Cardano’s formula, the predator-extinction periodic solution is derived for three different cases. The conditions for the global asymptotic stability of the predator-extinction periodic solution and for the permanence of the model are obtained using Floquet theory and the comparison theorem of impulsive differential equations, respectively. Furthermore, using bifurcation theory with the impulsive period as a parameter, we establish conditions under which the system bifurcates from a predator-extinction periodic solution to a positive periodic solution, signifying prey–predator coexistence as the impulsive period crosses a critical value. To demonstrate the main results and investigate the effects of the impulsive control period and the maximum prey release amount on the dynamic behavior of the investigated model, numerical simulations are conducted. The results show that both the impulsive period and the maximum prey release amount significantly affect the dynamic behavior of the model. These findings provide a reliable theoretical basis for practical aquaculture management.

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.