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. Here, we describe a case of severe skeletal bimaxillary protrusion treated with nonsurgical orthodontic treatments, highlighting an alternative treatment option. CASE SUMMARY: A 31-year-old woman wished to address a severe protrusion profile (approximately 8 mm overbite) and gummy smile. Cephalometric analysis and superimposition showed a severe skeletal class II pattern with a mandibular retrusion, and proclined and protrusive mandibular incisors. Panoramic radiograph showed a missing mandibular right third molar. A diagnosis of severe bimaxillary dentoalveolar protrusion was made. Taking into account the patient's fear of orthognathic surgery, she accepted the proposed alternative treatment using micro-implants and a self-made four-curvature torquing auxiliary. The treatment allowed for maximal en masse anterior tooth retraction, proper relocation of incisors, and alleviation of the skeletal class II pattern. Esthetically, the patient's lip protrusion was significantly decreased as was the overjet (from 10.5 mm to 1.8 mm), and the results remained stable throughout the 2-year follow-up. CONCLUSION: Nonsurgical treatment using micro-implants and a four-curvature torquing auxiliary may benefit severe cases of skeletal bimaxillary protrusion in adults.

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. To evaluate the performance of the improved algorithm, the TSWOA algorithm is employed to train the multi-layer perceptron (MLP) neural network. It is a difficult thing to propose a well-pleasing and valid algorithm to optimize the multi-layer perceptron neural network. Fifteen different data sets were selected from the UCI machine learning knowledge and the statistical results were compared with GOA, GSO, SSO, FPA, GA and WOA, severally. The statistical results display that better performance of TSWOA compared to WOA and several well-established algorithms for training multi-layer perceptron neural networks.

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. The experimental results show that the proposed ISHO algorithm performed better than other algorithms in terms of the solution precision and convergence rate.