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Purpose. This study investigated the efficacy of nanohydroxyapatite- (nHA-) coated biological prosthesis combined with platelet-rich plasma (PRP) in hemi hip replacement of femoral neck fracture (FNF) in the elderly. Methods. From September 2018 to September 2021, 102 elderly patients with FNF treated in our hospital were chosen and divided into two groups according to different intervention methods. Fifty-one patients in the bone cement group were treated with bone cement prosthesis, and the rest 51 patients in the observation group were treated with nHA biological prosthesis combined with PRP in hemi hip replacement. In order to explore the osteogenic effect of nHA and PRP, osteoblasts were cultured. Results. It was found that nHA and PRP could both effectively promote the proliferation of osteoblasts and improve their mineralization ability, especially when used in combination. In the course of clinical therapy, we found that the use of biological prosthesis combined with PRP could effectively reduce the level of serum procollagen type I carboxy terminal peptide (PICP) and better improve the levels of bone alkaline phosphatase (BALP) and bone Gla protein (BGP), so as to reduce the bone conversion rate and promote the formation of new bone around the prosthesis. In addition, no significant difference was found in intraoperative bleeding, operation time, hospital stay, 48 h drainage volume, partial weight-bearing time, and complete weight-bearing time between two groups. Otherwise, the use of biological prosthesis could effectively avoid the occurrence of adverse reactions such as bone cement crisis and fracture around femoral prosthesis, so as to better restore the hip function and improve patients’ life quality. Conclusions. Therefore, in hemi hip replacement of FNF in the elderly, nHA biological prosthesis combined with PRP can effectively promote the formation of new bone around the prosthesis stem, so as to obtain good initial stability, enable patients to carry out early weight-bearing exercise, and effectively avoid adverse reactions caused by bone cement prosthesis, thus improving patients’ hip function and life quality.

In this paper, an intelligent training model is designed for the OJ (Online Judge) system. In the traditional OJ system, students practice on a problem-by-problem basis, and get a score for completing a problem. In the intelligent training mode, all the problems are divided into categories according to knowledge points, and students practice on a category-by-category basis, and get a score for completing a category. Each student has to complete a different number of problems in the same category (the system will intelligently determine whether the student has met the passing conditions), thus achieving the goal of personalized training in a hierarchical manner. In order to reduce the teachers’ workload and improve the classification accuracy, the similarity matrix is constructed based on the similarity of the abstract syntax tree (AST) of the problems’ source code, and the problems are clustered by the K-Means algorithm, and good results are obtained.

We proposed a plagiarism detection approach based on code similarity and student behavior characteristics in educational scenarios. The traditional plagiarism check is based on the code only, which enables that students can escape inspection by modifying a small amount of code. We proposed that if the behavioral characteristics of students when submitting code can be considered, the suspected plagiarism can be more accurately identified. We proposed the concept of code similarity concentration (SCD) with reference to the Gini coefficient idea. SCD can reflect the similarity distribution between all the codes submitted by a student and others' codes. A large value of SCD means that a student's codes are always the most similar to the codes of some particular classmates. In addition, we also extracted other features to help detection. Finally, we classify the plagiarism detection problem as a binary classification problem and use LightGBM to make decisions. The experimental results show that the accuracy is close to 99% and f1-score is close to 98%.