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Reinforced concrete and steel (RCS) composite moment frame structures consist of reinforced concrete (RC) columns and steel (S) beams. Such systems combined with several advantages of the two structural members have larger structural stiffness, lower cost, and faster construction speed than the traditional concrete or steel frame system to be a particularly well viable alternative for use in seismic risk regions. The calculation of joint shear capacity is an essential step in seismic design. This study introduces the typical configuration characteristics of the RCS connection and reviews the state of the art of the shear bearing capacity in terms of failure models, shear distribution mechanisms, calculation mechanisms, and requirements given in the experimental and theoretical study to provide reference and foundation for the subsequent study. Finally, the development of recommendations and further research studies on seismic performance of RCS composite frames are provided. [ABSTRACT FROM AUTHOR]

This paper aimed to explore dexmedetomidine combined local anesthetics in brachial plexus block through ultrasound imaging (UI) under global joint entropy algorithm. Patients who underwent upper limb surgery and brachial plexus block were selected as research objects. Patients in group A were given 0.375% ropivacaine and normal saline, and patients in group B were given 0.375% ropivacaine and 1.0 μg/kg dexmedetomidine. The results of UI were analyzed by global joint entropy-based K-means clustering (GKC) algorithm, and the use effects of the two methods were compared in combination with other postanesthesia manifestations. The results were as follows. The segmentation accuracy (96.21% and 83.52%) of GKC was higher than 82.21% and 70.52% of the local joint entropy-based K-means clustering (LKC) (P < 0.05). The duration of sensory and motor block (352.78 ± 45.89 min and 324.38 ± 41.29 min) in group B was significantly longer than 292.28 ± 35.69 min and 256.58 ± 42.76 min in group A (P < 0.05). Compared with 84.91 ± 8.77 beats/min and 89.58 ± 7.62 beats/min in group A, mean arterial pressure (70.24 ± 9.77 beats/min and 69.89 ± 8.97 beats/min) in group B was lower at T1 and T2 (P < 0.05). The duration of postoperative pain (582.70 ± 51.89 min) in group B was longer than 372.89 ± 49.89 min in group A (P < 0.05). The postoperative pain score (2.98 ± 1.08) in group B was significantly lower than 4.48 ± 2.19 in group A (P < 0.05). Therefore, dexmedetomidine combined local anesthetics could prolong the duration of sensory and motor nerve block. Besides, dexmedetomidine had sedative and analgesic effects, so as to prolong postoperative pain time and reduce pain degree of patients. [ABSTRACT FROM AUTHOR]

This work was aimed at investigating image feature recognition and clinical nursing of children's rheumatoid arthritis- (CRA-) related lung injury under maximum correlation minimum redundancy algorithm of machine learning. In this study, 18 children with CRA in the hospital were selected as the rheumatoid group to explore the nursing method, and 18 healthy children were selected as the control group. The maximum correlation minimum redundancy algorithm of machine learning was compared with the information gain algorithm and the Fisher score algorithm and applied in computed tomography (CT) images of 18 CRA children. The classification accuracy of the algorithm in this study (94.52%) was higher than that of the information gain algorithm (88.64%) and Fisher score algorithm (81.24%). CT alveolitis score (2.35 ± 0.72 points) of children from the rheumatoid group was markedly higher than that of the control group (1.21 ± 0.24 points) (t = 2.147 and P < 0.05). The nitric oxide level (14.00 ppb) of children from the rheumatoid group increased greatly compared with the control group (10.00 ppb) (P < 0.05). CRA can cause a decline of lung function in children, while the nitric oxide level exhaled by children can assess the activity of RA. In addition, adopting active nursing methods can help children get better. [ABSTRACT FROM AUTHOR]

In this paper, we propose an Attentional Concatenation Generative Adversarial Network (ACGAN) aiming at generating 1024 × 1024 high-resolution images. First, we propose a multilevel cascade structure, for text-to-image synthesis. During training progress, we gradually add new layers and, at the same time, use the results and word vectors from the previous layer as inputs to the next layer to generate high-resolution images with photo-realistic details. Second, the deep attentional multimodal similarity model is introduced into the network, and we match word vectors with images in a common semantic space to compute a fine-grained matching loss for training the generator. In this way, we can pay attention to the fine-grained information of the word level in the semantics. Finally, the measure of diversity is added to the discriminator, which enables the generator to obtain more diverse gradient directions and improve the diversity of generated samples. The experimental results show that the inception scores of the proposed model on the CUB and Oxford-102 datasets have reached 4.48 and 4.16, improved by 2.75% and 6.42% compared to Attentional Generative Adversarial Networks (AttenGAN). The ACGAN model has a better effect on text-generated images, and the resulting image is closer to the real image. [ABSTRACT FROM AUTHOR]