Complexity最新文献

This paper proposes a deterministic nonlinear epidemic model, $$, incorporating media coverage for the analysis of infectious disease transmission dynamics, considering quarantine and isolation control strategies in a community with pre-existing immunity, and taking into account both asymptomatic and symptomatic infections. The model examines two equilibria: the disease-free equilibrium and a unique endemic equilibrium, investigating their existence and local stability concerning the effective reproduction number, R_e. In addition, global stability analysis of these equilibria is conducted. Media coverage is found to have no effect on R_e but plays a crucial role in mitigating the disease burden by reducing the number of infectious individuals at the endemic steady state and decreasing the infection peak. A new method is introduced to estimate the coefficients of media coverage. Based on the findings of central manifold theory, it is observed that when R_e exceeds unity, a transcritical bifurcation takes place in the system, leading to the asymptotic stability of the endemic equilibrium. Moreover, it is noted that the effectiveness of quarantine and isolation at the population level depends on the transmission rate among isolated and quarantined individuals, while higher pre-existing immunity in the population reduces the infection peak and hastens its occurrence. Theoretical results are validated through simulations, and sensitivity analysis is conducted for the effectiveness of R_e with respect to model parameters.

This study introduces a novel evolutionary game-based model to explore the dynamic interplay between venture capital (VC) institutions and their invested firms in adopting corporate social responsibility (CSR) practices, with a focus on the moderating role of government supervision. With CSR gaining prominence in VC-backed firm ecosystems, understanding these dynamics is critical. Verified through numerical simulations, the model examines system evolution under scenarios with and without government supervision. Key findings include the following: (1) VC-backed firms are more likely to adopt CSR practices when VCs prioritize long-term investment strategies, whereas short-term strategies centered on shareholder value hinder CSR efforts; (2) factors such as VC equity share, CSR costs, and benefits significantly influence CSR adoption; and (3) government incentives can promote CSR and accelerate convergence toward ideal outcomes, though with limited impact. This research advances the understanding of the VC-CSR relationship, offering theoretical insights and practical recommendations to foster responsible investment and business practices.

Enterprise sponsored virtual communities (ESVC) are important platforms for user knowledge sharing. User characteristics, as influential factors in these communities, impact the evolution of knowledge sharing processes. In this study, a knowledge sharing evolutionary game model that considers the influence of user characteristics was constructed. A payoff matrix was established, and stable points were identified for further analysis. Simulation analysis was conducted to explore the impact of changes in user characteristics on the knowledge sharing strategies of both parties. This study revealed the following findings: First, the knowledge reservoir of ESVC users affects their willingness to share knowledge, with ordinary users’ willingness initially increasing and then decreasing as their knowledge reservoirs grow. Second, the willingness of leading users to share knowledge initially decreases and then increases as their knowledge reservoirs expand. Third, improvements in the knowledge absorption and transformation capabilities of both leading and ordinary users promote knowledge sharing behaviour, although the extent of this impact differs between the two groups.

In recent years, there has been a marked surge in research focused on nonlinear oscillators. Among these, a particular emphasis has been placed on a class of oscillators distinguished by their concealed attractors, drawing considerable attention due to their unique characteristics. This paper delves into the exploration of an elegant oscillator belonging to this distinctive class. Despite comprising five terms and lacking equilibrium points, this oscillator displays remarkably intricate dynamics. The study covers various aspects such as chaos, hidden attractors, offset boosting, and notably, different strange attractors exhibited by this oscillator. Additionally, approaches involving synchronization for such oscillators are introduced. Apart from the presentation of the novel chaotic oscillator, the synchronization of a nominal and uncertain chaotic system is evinced by the sliding mode technique (super-twisting algorithm) in the first case, and a robust controller is synthesized, respectively. The appropriate Lyapunov functions are implemented in the two synchronization strategies leading to obtain suitable control strategies to achieve fast and accurate control laws. The respective simulations are performed along with the conclusions of this work.

Nanomaterials find application in electronics, drugs, and biology among other disciplines. Benzenoid systems with their homogeneous structures are especially fit for computer study because of their predictable geometries. This work investigates computational analysis of benzenoid systems using valency-based entropy measurements and degree-based topological indices. Knowing these indices helps one to anticipate the reactivity and stability of related compounds. The main focus of the work is on the thermodynamic parameter entropy, which reveals how one can modify the benzenoid hydrocarbon structures to improve their thermodynamic characteristics. In this work, topological indices combined with entropy measurements facilitates prediction and enhancement of benzenoid system physicochemical features. This helps one to grasp their possible applications in nanotechnology and medicine.

In deep learning, the choice of activation function plays a vital role in enhancing model performance. We propose AHerfReLU, a novel activation function that combines the rectified linear unit (ReLU) function with the error function (erf), complemented by a regularization term 1/(1 + x²), ensuring smooth gradients even for negative inputs. The function is zero centered, bounded below, and nonmonotonic, offering significant advantages over traditional activation functions like ReLU. We compare AHerfReLU with 10 adaptive activation functions and state-of-the-art activation functions, including ReLU, Swish, and Mish. Experimental results show that replacing ReLU with AHerfReLU leads to 3.18% improvement in Top-1 accuracy on the LeNet network for the CIFAR100 dataset, 0.63% improvement on CIFAR10%, and 1.3% improvement in mean average precision (mAP) on the SSD300 model in the Pascal VOC dataset. Our results demonstrate that AHerfReLU enhances model performance, offering improved accuracy, loss reduction, and convergence stability. The function outperforms existing activation functions, providing a promising alternative for deep learning tasks.

This paper designs an optimal control strategy for the auto-berthing control (ABC) problem of underactuated surface vessels. The purpose is to achieve accurate berthing of USVs in complex environments. In the traditional ABC problem of surface vessels, the underactuated characteristics of USVs complicate the control design. This underactuated phenomenon is manifested in the lack of lateral driving force on the boat, which restricts its motion to a specific direction. In order to solve the problem of the lack of lateral driving force of USVs, this paper first uses diffeomorphism transformation to transform the original nonholonomic constraint system into a chain structure. Through this transformation, the problem of the state of the vessel being restricted during motion is avoided. On this basis, this paper further describes the optimal control problem of the USV on the horizontal plane based on the kinematic and dynamic models of the USV. In order to solve the optimal control problem, this paper adopts the Gaussian pseudospectral method (GPM). By discretizing the continuous optimal control problem into a nonlinear programming problem, the computational complexity is effectively reduced and the solution efficiency is improved. The optimal navigation trajectory of the USV system and the corresponding optimal control input are then obtained. In order to verify the feasibility and effectiveness of the proposed control strategy, numerical simulation is carried out. The simulation results show that the optimal control strategy can achieve stable and accurate berthing of USVs.

Tracking athletes in high-speed outdoor sports like alpine skiing causes substantial difficulties because of ever-changing movements, environmental variability, and the limitations of traditional tracking technologies, such as intrusive sensors and single-view camera setups. This study proposes a hybrid approach for tracking alpine skiing activities by combining YOLO-v8 with an evolutionary version of the chimp optimization algorithm (CHOA-EVOL) for optimizing hyperparameters. The primary goal of this research is to enhance the CHOA to optimally adjust the hyperparameters of YOLO-v8, consequently addressing the drawbacks of outdoor sports tracking technology. This hybrid model integrates data from unmanned aerial vehicles (UAVs) and terrestrial cameras to better understand athletes’ rapid rotating motion. The suggested approach is extensively tested and validated using advanced algorithms with the UAV123 dataset and a recently developed alpine skiing dataset (ASD). The results have shown that our proposed approach can achieve high precision and robustness.

The existence of clearance in the joint causes positional deviation and reduces the accuracy of robotics, in which nonlinear factors such as friction and lubrication inside the joint seriously affect its dynamic behaviors. On the other hand, the effectiveness of control algorithms is largely dependent on the dynamic complexity of the robotics, while its complexity analysis is an important prerequisite for achieving high-performance control. In this paper, we provide a method for analyzing the dynamic complexity of a planar manipulator with clearance and lubrication in joints based on a nonlinearity measure. The influences of joint parameters such as friction coefficient, lubricant viscosity, and gap radius are quantified on the dynamic complexity of the kinetics, which can identify the collision state of the joints effectively in the robot’s motion. First, a dynamical model of a planar manipulator is established by integrating the contact-separation model and the force model at the joint, and a method for calculating the dynamic complexity based on the nonlinearity measure is proposed. Second, the effects of different joint parameters on the dynamic behaviors of the robotic system are analyzed, and the relationships between the lubricant viscosity, gap radius, and the dynamic complexity under different friction coefficients are established to analyze the impacts of the joint parameters on the dynamic complexities. The results show that the dynamic complexity of the robotic system can be decreased significantly by using the small gap radius and high viscosity of the lubricant, which can help to realize the better control performance. Especially, this method is more sensitive to state changes such as the collision state of the joint relative to the sample entropy method.

Inspired by the genetic evolution mechanism of DNA, a hybrid DNA Gray Wolf Optimizer (hDNA-GWO) is proposed to develop an accurate kinetic model. This algorithm incorporates innovative DNA encoding, selection, crossover, and mutation operators inspired by genetic processes. We adopt the roulette-wheel method to select individuals with greater environmental adaptability from the current population to form the next population. The crossover operation involves swapping gene segments between paired chromosomes to create new individuals and maintain the population diversity. The mutation operator can maintain the diversity of the population, avoid the phenomenon of “premature” convergence, and effectively improve the local search capability. The performance of hDNA-GWO is investigated on typical benchmark functions compared to GWO, PSO, GWO-PSO, and GWO-GA. In addition, the superior search capabilities of our model are validated by kinetic parameter estimation using experimental data from supercritical water oxidation processes. The results indicate that the hDNA-GWO can overcome premature convergence and obtain higher-quality global optimal solutions.