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Background. The screw and cement technique is a convenient method used to rebuild medial tibial plateau defects in primary total knee arthroplasty (TKA). The objective of this study was to perform a finite element assessment to determine the effect of different numbers of screws on the stability of TKA and to determine whether differences exist between two different insertion angles. Method. Six tibial finite element models with defects filled with screws and cement and one model with defects filled only with cement were generated. Contact stresses on the surface of cancellous bone in different areas were calculated. Results. Compared to the cement-only technique, the stress on the border of cancellous bone and bone cement decreased by 10% using the screw and cement technique. For bone defects with a 12% defect area and a 12-mm defect depth, the use of 1 screw achieved the greatest stability; for those with a 15% defect area and a 20-mm defect depth, 2 screws achieved the greatest stability. Conclusions. (1) The screw and cement technique is superior to the bone cement-only technique. For tibial defects in which the defect area comprises a large percentage but the depth is less than 5 mm, the screw and cement technique is recommended. (2) Vertical screws can achieve better stability than oblique screws. (3) Screws should be used in moderation for different defects; more is not always better.

The “classical pattern” of stock price formation has long been widely used in the determination of future price trends of stocks, and the identification and analysis of classical price patterns have an important guiding role in investors’ decision-making and trading. The wavelet transform is a useful tool to remove some of the noise of time series because it has the characteristic of multiresolution. In this study, we propose a method for stock price pattern recognition based on the wavelet transform and dynamic time warp (DTW). A pattern recognition method with similar quantified results is developed to obtain accurate pattern recognition results. That is, using the wavelet transform to smooth the original price graph, and then using the DTW algorithm improved in this study to find the graph with the smallest distance from the target graph under the sliding window method, the identification and analysis of the target graph can be realized. In order to improve the recognition rate of the target graph, we preprocessed the raw price sequence using the moving average convergence and divergence (MACD) algorithm based on the control experiments set up in this study. The pattern recognition method used in this study will identify the price patterns of a certain time window as a whole, thus avoiding the problem of how to objectively select the important points that constitute a price pattern and the mathematical definition of different price patterns in the previous traditional methods.