Finite time boundedness of neutral high-order Hopfield neural networks with time delay in the leakage term and mixed time delays

A Rare Hernia Mimicking Implant in a Patient with Rectal Adenocarcinoma: Internal Herniation

Internal herniation may be seen more frequently in patients with intra-abdominal surgery and malignancy history. We presented a 58-year-old male patient diagnosed with rectal adenocarcinoma seven years ago with a history of surgery and pelvic radiotherapy. When the abdominal computed tomography (CT) image was taken during routine oncology follow-up, a lesion mimicking a serosal implant on the anterior abdominal wall was detected. ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT imaging was performed the suspicion of recurrence. It was concluded that the lesion, which was evaluated as an implant in abdominal CT with ¹⁸F-FDG PET/CT imaging, was a spontaneously reducing internal herniation. ¹⁸F-FDG PET/CT imaging in cancer patients is crucial in illuminating the suspicion of recurrent lesions in these patients and sheds light on the course of the patients in oncology practice.

Dynamical behavior of recurrent neural networks with different external inputs

The main aim of this paper is to study the dynamics of a recurrent neural networks with different input currents in terms of asymptotic point. Under certain circumstances, we studied the existence, the uniqueness of bounded solutions and their homoclinic and heteroclinic motions of the considered system with rectangular currents input. Moreover, we studied the unpredictable behavior of the continuous high-order recurrent neural networks and the discrete high-order recurrent neural networks. Our method was primarily based on Banach’s fixed-point theorem, topology of uniform convergence on compact sets and Gronwall inequality. For the demonstration of theoretical results, we give examples and their numerical simulations.

Stabilization of Second-Order Memristive Neural Networks With Mixed Time Delays via Nonreduced Order

In this brief, we investigate a class of second-order memristive neural networks (SMNNs) with mixed time-varying delays. Based on nonsmooth analysis, the Lyapunov stability theory, and adaptive control theory, several new results ensuring global stabilization of the SMNNs are obtained. In addition, compared with the reduced-order method used in the existing research studies, we consider the global stabilization directly from the SMNNs themselves without the reduced-order method. Finally, we give some numerical simulations to show the effectiveness of the results.