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A binary artificial rabbit optimization algorithm is proposed to maximize the cognitive radio (CR) network performance and fairness among users by addressing the issue of low spectrum resource utilization during the CR spectrum allocation process. Building upon the graph coloring spectrum allocation model, Sigmoid is introduced to transform the algorithm solution space. Incorporating the Lévy flight strategy for adaptive step size adjustment enhances the algorithm’s flexibility and convergence accuracy. Moreover, a selective opposition strategy based on Spearman’s coefficient is integrated into the algorithm to improve population diversity and convergence accuracy through reverse learning. Applying the binary artificial rabbit optimization algorithm to the CR spectrum allocation problem in the same CR network environment, we compare network efficiency and inter-user fairness through simulation experiments with the artificial rabbits optimization algorithm seagull optimization algorithm and particle swarm optimization algorithms. The experimental results show that the binary artificial rabbit optimization algorithm has higher convergence performance and global exploration ability, improves the overall network efficiency and user fairness of CR networks, and alleviates the problem of low spectrum resource utilization.

In this paper, we provide a rigorous mathematical foundation for continuous approximations of a class of systems with piecewise continuous functions. By using techniques from the theory of differential inclusions, the underlying piecewise functions can be locally or globally approximated. The approximation results can be used to model piecewise continuous-time dynamical systems of integer or fractional-order. In this way, by overcoming the lack of numerical methods for differential equations of fractional-order with discontinuous right-hand side, unattainable procedures for systems modeled by this kind of equations, such as chaos control, synchronization, anticontrol and many others, can be easily implemented. Several examples are presented and three comparative applications are studied.

Endocardial and epicardial border identification has been of extensive interest in cardiac Magnetic Resonance Images (MRIs). It is a difficult job to segment the epicardium and endocardium accurately and automatically from cardiac MRI owing to the cardiac tissues’ complexity even though the prevailing Deep Learning (DL) methodologies had attained significant success in medical imaging segmentation. Hence, by employing effectual ResNeXT-50-centric Inverse Edge Indicator Level Set (IEILS) and anisotropic sigmoid diffusion algorithms, this system has proposed cardiac MRI segmentation. The work has endured some function for an effectual partition of epicardium and endocardium. Initially, by employing the Truncated Kernel Function (TK)-Trilateral Filter, the noise removal function is executed on the input cardiac MRI. Next, by wielding the ResNeXT-50 IEILS, the Left and Right Ventricular (LV/RV) regions are segmented. The epicardium and endocardium are segmented by the ASD algorithm once the LV/RV is separated from the Left Ventricle (LV) region. Here, the openly accessible Sunnybrook and the Right Ventricle (RV) datasets are wielded. Then, the prevailing state-of-art algorithms are analogized to the outcomes achieved by the proposed framework. Regarding accuracy, sensitivity, and specificity, the proposed methodology executed the cardiac MRI segmentation process precisely along with the other surpassed state-of-the-art methodologies.