Complexity最新文献

In this publication, we present a model that simulates the spread of monkeypox virus. The model takes into account the effect of interactions between human and rodent populations, as well as certain realistic factors such as the complex network with a heterogeneous mean field and certain disease control measures such as vaccination and disease therapy. Although vaccines do not offer total protection against the disease, they can reduce the transmission of monkeypox, which could help reduce the spread of the virus in a population, as our results show. So to develop efficient strategies for epidemic control and prevention, we studied global sensitivity analysis to determine the parameters most influential on disease spread. The existence and overall stability of the disease-free equilibrium are also discussed. The basic reproduction number R₀ of the model is calculated. The results show that if R₀ < 1, the disease-free equilibrium of the model is globally asymptotically stable. In addition, the model’s key parameters are calculated using a least-mean-squares curve fit to the real data.

Controllability in temporal networks is considered one of the most important challenges in this type of network. Network controllability methods try to fully control the network with the minimum number of control nodes. This type of network is always exposed to internal and external attacks and failures. Therefore, controllability processes need recovery mechanisms to be resistant to various types of failures. The high volume of temporal networks causes the recovery controllability processes to be disrupted. In the paper, a novel method of recovery controllability in temporal networks is proposed to improving controllability network robustness. To restore controllability of the network, the RCTE framework is proposed, in which a temporal network is converted into snapshots at discrete times and then its dimensions are reduced by using network embedding. Finally, using link prediction based on local and global similarity, links that are subject to failure are identified. The effectiveness of the proposed method against various network attacks has been evaluated and compared with other conventional methods. The results show that the RCTE framework performed better than other conventional methods. Also, the proposed method has more controllability and tolerance against malicious attacks compared to other recovery methods.

This study aims to evaluate quantitatively (albeit in arbitrary units) the evolution of complexity of the human system since the domestication of fire. This is made possible by studying the timing of the 14 most important milestones—breaks in historical perspective—in the evolution of humans. AI is considered here as the latest such milestone with importance comparable to that of the Internet. The complexity is modeled to have evolved along a bell-shaped curve, reaching a maximum around our times, and soon entering a declining trajectory. According to this curve, the next evolutionary milestone of comparable importance is expected around 2050–2052 and should add less complexity than AI but more than the milestone grouping together nuclear energy, DNA, and the transistor. The peak of the complexity curve coincides squarely with the life span of the baby boomers. The peak in the rate of growth of the world population precedes the complexity peak by 25 years, which is about the time it takes a young man or woman before they are able to add complexity to the human system in a significant way. It is in society’s interest to flatten the complexity bell-shaped curve to whatever extent this is possible in order to enjoy complexity longer.

Violence in Mexico presents a significant challenge to public safety and social stability and requires a comprehensive understanding of its temporal dynamics. This study uses a complex temporal networks approach to analyze patterns of violence across municipalities, focusing on crimes such as homicide, sexual assault, discrimination, and armed robbery. Using data spanning 6 years (2015–2021), this study builds temporal networks to find fundamental patterns and trends in violence. Then, using advanced network analysis techniques, including robustness assessment and influence quantification, key influential municipalities were identified within the violence network. The findings of this research can offer valuable information to carry out specific interventions and resource allocation strategies to improve public safety in Mexico.

Nonlinear science represents a foundational frontier in scientific inquiry that explores the shared characteristics inherent in nonlinear phenomena. This study focused on the perturbed Boussinesq (PB) equation incorporating dual perturbation terms. Soliton solutions were deduced by leveraging the traveling wave hypothesis. Furthermore, by employing the generalized Jacobi elliptic expansion function (JEEF) method and the improved tan (Λ/2) method, diverse nonlinear wave solutions, including kink, dark, periodic, bright, singular, periodic waves, bell-shaped solitons, solitary waves, shock waves, and kink-shaped soliton solutions, were acquired. The establishment of constraint relations is detailed to delineate the criteria for the existence of these wave solutions. Notably, these solutions are innovative and present novel contributions that have not yet been documented in the literature. In addition, 2D and 3D graphics were constructed to visually elucidate the physical behavior inherent to these newly acquired exact solutions.

In this work, the population dynamics of psychoactive substance abuse model governed by a system of nonlinear ordinary differential equations is studied through the efficacy and economic analyses. The study considers three time-variant intervention strategies, such as screening of addicted immigrants, advocacy against substance abuse and rehabilitation therapy for the control of substance abuse spread in the population. Most sensitive parameters to be targeted by the control interventions are examined through sensitivity analysis using the normalized forward sensitivity index. By employing optimal control theory, the existence of optimal control is qualitatively analysed and the control triple is characterized using Pontryagin’s maximum principle. To further assess the impact of the intervention strategies, three different policies combining any two of the intervention strategies, namely, Policy A (combination of screening of addicted immigrants and advocacy against substance abuse), Policy B (combination of screening of addicted immigrants and rehabilitation therapy) and Policy C (combination of advocacy against substance abuse and rehabilitation therapy), are considered. Efficacy and economic analyses are extensively conducted on each of the policies, and it is revealed that Policy B is the most efficient intervention, while Policy C is the most cost-effective intervention. As a result, the policy that averts the highest number of psychoactive substance abuse cases when limited resources are available is recommended.

Bridge inspection text records are very significant for the maintenance and upkeep of bridges, which can help engineers and maintenance personnel to understand the actual condition of bridges, detect and repair problems in time, and ensure the safe operation of bridges. Currently, more and more research focuses on how to extract potentially valuable bridge-related information from bridge inspection texts. In this study, we take the bridge inspection domain machine-reading comprehension corpus as the data support for model training and performance evaluation; oriented to the bridge inspection domain data text extraction machine-reading comprehension task, on the basis of word-granularity text input, we further explore two schemes of co-occurring linkage of cross-sentence entities in the context and co-occurring linkage of entities within the sentence through graph structure, and we learn and extract naming through graph-attentive neural networks-structured semantic information between entities and fused the obtained named entity embeddings with a hidden representation of the pretrained context. Tested on the bridge inspection domain dataset, the integrated model proposed in this research improves the EM optimum by 1.4% and the mean by 2.2% and the F1 optimum by 2.2% and the mean by 1.6% on the BIQA test, compared with the better-performing baseline model RoBERTa_wwm_ext.

The widespread use of the internet has led to the rapid expansion of social networks, making it easier for individuals to share content online. However, this has also increased the prevalence of cyber violence, necessitating the development of automated detection methods. Deep learning-based algorithms have proven effective in identifying violent content, yet existing models often struggle with understanding contextual nuances and implicit forms of cyber violence. To address this limitation, we propose a novel deep multi-input recurrent neural network architecture that incorporates neighborhood-based contextual information during training. The Jaccard similarity metric is employed to construct neighborhoods of input texts, allowing the model to leverage surrounding context for improved feature extraction. The proposed model combines Bi-LSTM and GRU networks to capture both sequential dependencies and contextual relationships effectively. The proposed model was evaluated on a real-world cyber violence dataset, achieving an accuracy of 94.29%, recall of 81%, precision of 72%, and an F1-score of 76.23% when incorporating neighborhood-based learning. Without contextual information, the model attained an accuracy of 89.15%, recall of 72.00%, precision of 71.5%, and an F1-score of 71.74%. These results demonstrate that neighborhood-based learning contributed to an average improvement of 5.14% in accuracy and 4.49% in F1-score, underscoring the importance of contextual awareness in cyber violence detection. These results highlight the significance of contextual awareness in deep learning-based text classification and underscore the potential of our approach for real-world​ applications.

This study explores the dynamics of a discrete-time predator–prey system, incorporating a Holling type III functional response and prey refuge, through the piecewise constant argument method. This method keeps things consistent and prevents negative population values, which is often a drawback of older techniques. We take a closer look at fixed points, exploring their existence and stability. We identify the conditions that lead to period-doubling and Neimark–Sacker bifurcations, and we back up our findings with numerical simulations. Our findings emphasize how important the predation coefficient is for keeping ecological balance and show that, in this model setup, the refuge for prey has a minimal effect on the stability of the system. These insights help us better understand the relationships between predators and their prey, providing valuable guidance for conserving biodiversity and managing ecosystems.