目的比较面部SMAS筋膜除皱术复合面部脂肪抽吸术与传统单纯面部SMAS筋膜除皱术的临床疗效及安全性。方法选取2021年6月至2023年6月河北承德玥莱美整形医院收治的54例中重度面部皮肤松弛患者,分为对照组(27例,行传统单纯面部SMAS筋膜除皱...目的比较面部SMAS筋膜除皱术复合面部脂肪抽吸术与传统单纯面部SMAS筋膜除皱术的临床疗效及安全性。方法选取2021年6月至2023年6月河北承德玥莱美整形医院收治的54例中重度面部皮肤松弛患者,分为对照组(27例,行传统单纯面部SMAS筋膜除皱术)与观察组(27例,行面部SMAS筋膜除皱术复合面部脂肪抽吸术)。比较两组患者手术时间、术后肿胀恢复时间、除皱效果、长期效果(效果持久性)及患者满意度。结果对照组手术时间(4.0±0.5)h短于观察组(4.5±0.5)h,差异有统计学意义(P<0.01),对照组术后肿胀恢复时间(12.9±5.85)d短于观察组(15.8±8.05)d,差异有统计学意义(P=0.04)。观察组在改善口周隆起(0 vs 55.55%)、效果持久性(11.1%vs 37.0%)及患者满意度(96.3%vs 51.9%)方面均显著优于对照组,差异有统计学意义(P<0.05)。两组均未发生切口感染等严重并发症。结论面部SMAS筋膜除皱术复合面部脂肪抽吸术,可有效改善面颊和口周脂肪堆积,提升面部皮肤平整度与紧致度,延缓面部皮肤松弛进程,效果更持久,美学效果更佳,患者满意度更高。虽然手术时间及术后肿胀恢复期略长,但整体是面部SMAS筋膜除皱术的理想优化术式,值得推广。展开更多
Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convol...Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.展开更多
文摘目的比较面部SMAS筋膜除皱术复合面部脂肪抽吸术与传统单纯面部SMAS筋膜除皱术的临床疗效及安全性。方法选取2021年6月至2023年6月河北承德玥莱美整形医院收治的54例中重度面部皮肤松弛患者,分为对照组(27例,行传统单纯面部SMAS筋膜除皱术)与观察组(27例,行面部SMAS筋膜除皱术复合面部脂肪抽吸术)。比较两组患者手术时间、术后肿胀恢复时间、除皱效果、长期效果(效果持久性)及患者满意度。结果对照组手术时间(4.0±0.5)h短于观察组(4.5±0.5)h,差异有统计学意义(P<0.01),对照组术后肿胀恢复时间(12.9±5.85)d短于观察组(15.8±8.05)d,差异有统计学意义(P=0.04)。观察组在改善口周隆起(0 vs 55.55%)、效果持久性(11.1%vs 37.0%)及患者满意度(96.3%vs 51.9%)方面均显著优于对照组,差异有统计学意义(P<0.05)。两组均未发生切口感染等严重并发症。结论面部SMAS筋膜除皱术复合面部脂肪抽吸术,可有效改善面颊和口周脂肪堆积,提升面部皮肤平整度与紧致度,延缓面部皮肤松弛进程,效果更持久,美学效果更佳,患者满意度更高。虽然手术时间及术后肿胀恢复期略长,但整体是面部SMAS筋膜除皱术的理想优化术式,值得推广。
基金supported by Science and Technology Innovation Programfor Postgraduate Students in IDP Subsidized by Fundamental Research Funds for the Central Universities(Project No.ZY20240335)support of the Research Project of the Key Technology of Malicious Code Detection Based on Data Mining in APT Attack(Project No.2022IT173)the Research Project of the Big Data Sensitive Information Supervision Technology Based on Convolutional Neural Network(Project No.2022011033).
文摘Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.
