Anorectal malignant melanoma is a very rare but lethal disease. Patients with anorectal malignant melanoma commonly complain for changes in bowel habits and rectal bleeding. Therefore, anorectal malignant melanoma is ...Anorectal malignant melanoma is a very rare but lethal disease. Patients with anorectal malignant melanoma commonly complain for changes in bowel habits and rectal bleeding. Therefore, anorectal malignant melanoma is often misdiagnosed as hemorrhoids, polyp or rectal cancer. Surgery is the mainstay of treatment for patients with anorectal malignant melanoma. However, whether abdominoperineal resection or wide local excision is the most appropriate surgical approach is still a controversial issue. Recently, with the great development of laparoscopic techniques, more and more operations can be performed by laparoscopic techniques. However, laparoscopic abdominoperineal resection for management of anorectal malignant melanoma has been rarely reported. In this study, we reported 4 patients with anorectal malignant melanoma underwent laparoscopic abdominoperineal resection. The outcomes of these patients were relatively good during a long time follow-up. Meanwhile, we reviewed the relevant studies with particular focus surgical treatment.展开更多
Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it under...Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.展开更多
文摘Anorectal malignant melanoma is a very rare but lethal disease. Patients with anorectal malignant melanoma commonly complain for changes in bowel habits and rectal bleeding. Therefore, anorectal malignant melanoma is often misdiagnosed as hemorrhoids, polyp or rectal cancer. Surgery is the mainstay of treatment for patients with anorectal malignant melanoma. However, whether abdominoperineal resection or wide local excision is the most appropriate surgical approach is still a controversial issue. Recently, with the great development of laparoscopic techniques, more and more operations can be performed by laparoscopic techniques. However, laparoscopic abdominoperineal resection for management of anorectal malignant melanoma has been rarely reported. In this study, we reported 4 patients with anorectal malignant melanoma underwent laparoscopic abdominoperineal resection. The outcomes of these patients were relatively good during a long time follow-up. Meanwhile, we reviewed the relevant studies with particular focus surgical treatment.
基金This research was funded by the National Natural Science Foundation of China(42361041)the innovation project of education technology,Gansu Education Department(2022B-090).
文摘Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features.
