Analysis of lymph node metastasis and survival prognosis in early gastric cancer patients: A retrospective study.

Background: Early gastric cancer (EGC) is a common malignant tumor of the digestive system, and its lymph node metastasis and survival prognosis have been concerning. By retrospectively analyzing the clinical data of EGC patients, we can better understand the status of lymph node metastasis and its impact on survival and prognosis.

Aim: To evaluate the prognosis of EGC patients and the factors that affect lymph node metastasis.

Severe skeletal bimaxillary protrusion treated with micro-implants and a self-made four-curvature torquing auxiliary: A case report.

Background: Bimaxillary protrusion is a clinically common dentofacial deformity, particularly among Chinese patients. This kind of malformation can severely affect facial esthetics and, even in mild cases, is difficult to correct without surgery. Unfortunately, many patients abandon treatment because of fear of surgery.

Teaching learning-based whale optimization algorithm for multi-layer perceptron neural network training.

This paper presents an improved teaching learning-based whale optimization algorithm (TSWOA) used the simplex method. First of all, the combination of WOA algorithm and teaching learning-based algorithm not only achieves a better balance between exploration and exploitation of WOA, but also makes whales have self-learning ability from the biological background, and greatly enriches the theory of the original WOA algorithm. Secondly, the WOA algorithm adds the simplex method to optimize the current worst unit, averting the agents to search at the boundary, and increasing the convergence accuracy and speed of the algorithm.

An improved spotted hyena optimizer for PID parameters in an AVR system.

In this paper, an improved spotted hyena optimizer (ISHO) with a nonlinear convergence factor is proposed for proportional integral derivative (PID) parameter optimization in an automatic voltage regulator (AVR). In the proposed ISHO, an opposition-based learning strategy is used to initialize the spotted hyena individual's position in the search space, which strengthens the diversity of individuals in the global searching process. A novel nonlinear update equation for the convergence factor is used to enhance the SHO's exploration and exploitation abilities.