目的观察腹腔镜下穿孔修补术对老年胃溃疡合并胃穿孔患者术中出血量、疗效及并发症的影响,以寻求有效的疗法。方法选择陕西省榆林市第一医院2015年1月—2017年1月收治的80例老年胃溃疡合并胃穿孔患者,根据不同手术方式分为观察组(n=40)...目的观察腹腔镜下穿孔修补术对老年胃溃疡合并胃穿孔患者术中出血量、疗效及并发症的影响,以寻求有效的疗法。方法选择陕西省榆林市第一医院2015年1月—2017年1月收治的80例老年胃溃疡合并胃穿孔患者,根据不同手术方式分为观察组(n=40)和对照组(n=40)。观察组采取腹腔镜下穿孔修补术,对照组采用传统开腹穿孔修补术。对2组的临床疗效、术中术后指标和术后并发症发生情况进行比较与分析。结果观察组的总有效率显著高于对照组(92.5%vs70.0%,P<0.05),术中出血量显著少于对照组[(48.9±10.3)mL vs (72.5±16.2)mL,P<0.05)],肠鸣音恢复时间、肛门排气时间和住院时间均显著短于对照组(均<0.05),术后并发症发生率显著低于传统开腹组(2.5%vs37.5%,P<0.05)。结论腹腔镜下穿孔修补术较传统开腹穿孔修补术更能有效减少老年胃溃疡合并胃穿孔患者的术中出血量,提升疗效,减少并发症,是一种更有效的疗法。展开更多
Human Activity Recognition (HAR) is an important way for lower limb exoskeleton robots to implement human-computer collaboration with users. Most of the existing methods in this field focus on a simple scenario recogn...Human Activity Recognition (HAR) is an important way for lower limb exoskeleton robots to implement human-computer collaboration with users. Most of the existing methods in this field focus on a simple scenario recognizing activities for specific users, which does not consider the individual differences among users and cannot adapt to new users. In order to improve the generalization ability of HAR model, this paper proposes a novel method that combines the theories in transfer learning and active learning to mitigate the cross-subject issue, so that it can enable lower limb exoskeleton robots being used in more complex scenarios. First, a neural network based on convolutional neural networks (CNN) is designed, which can extract temporal and spatial features from sensor signals collected from different parts of human body. It can recognize human activities with high accuracy after trained by labeled data. Second, in order to improve the cross-subject adaptation ability of the pre-trained model, we design a cross-subject HAR algorithm based on sparse interrogation and label propagation. Through leave-one-subject-out validation on two widely-used public datasets with existing methods, our method achieves average accuracies of 91.77% on DSAD and 80.97% on PAMAP2, respectively. The experimental results demonstrate the potential of implementing cross-subject HAR for lower limb exoskeleton robots.展开更多
With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial informati...With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.展开更多
文摘目的观察腹腔镜下穿孔修补术对老年胃溃疡合并胃穿孔患者术中出血量、疗效及并发症的影响,以寻求有效的疗法。方法选择陕西省榆林市第一医院2015年1月—2017年1月收治的80例老年胃溃疡合并胃穿孔患者,根据不同手术方式分为观察组(n=40)和对照组(n=40)。观察组采取腹腔镜下穿孔修补术,对照组采用传统开腹穿孔修补术。对2组的临床疗效、术中术后指标和术后并发症发生情况进行比较与分析。结果观察组的总有效率显著高于对照组(92.5%vs70.0%,P<0.05),术中出血量显著少于对照组[(48.9±10.3)mL vs (72.5±16.2)mL,P<0.05)],肠鸣音恢复时间、肛门排气时间和住院时间均显著短于对照组(均<0.05),术后并发症发生率显著低于传统开腹组(2.5%vs37.5%,P<0.05)。结论腹腔镜下穿孔修补术较传统开腹穿孔修补术更能有效减少老年胃溃疡合并胃穿孔患者的术中出血量,提升疗效,减少并发症,是一种更有效的疗法。
文摘Human Activity Recognition (HAR) is an important way for lower limb exoskeleton robots to implement human-computer collaboration with users. Most of the existing methods in this field focus on a simple scenario recognizing activities for specific users, which does not consider the individual differences among users and cannot adapt to new users. In order to improve the generalization ability of HAR model, this paper proposes a novel method that combines the theories in transfer learning and active learning to mitigate the cross-subject issue, so that it can enable lower limb exoskeleton robots being used in more complex scenarios. First, a neural network based on convolutional neural networks (CNN) is designed, which can extract temporal and spatial features from sensor signals collected from different parts of human body. It can recognize human activities with high accuracy after trained by labeled data. Second, in order to improve the cross-subject adaptation ability of the pre-trained model, we design a cross-subject HAR algorithm based on sparse interrogation and label propagation. Through leave-one-subject-out validation on two widely-used public datasets with existing methods, our method achieves average accuracies of 91.77% on DSAD and 80.97% on PAMAP2, respectively. The experimental results demonstrate the potential of implementing cross-subject HAR for lower limb exoskeleton robots.
文摘With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.
