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在现有的信息素养评价指标体系中,假设各评价指标之间是不存在影响和依赖关系的,但这样的假设在实际中是不存在的.因此,考虑到各评价指标之间的相互影响,本文利用网络分析方法(ANP)建立了师范生信息素养评价指标体系的网络分析模型,并确定了各指标的相对权重,以期为师范生信息素养教育的侧重点提供参考.  相似文献   
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Paradoxical vocal fold motion is a rare disorder in which adduction of the folds occurs on inspiration. The disorder presents with signs of airway obstruction and often airway distress, so proper diagnosis by the otorhinolaryngologist is critical to subsequent management. We present a retrospective review of 10 patients with the diagnosis of paradoxical vocal fold motion seen over a 6-year period. Eight patients were females, and 6 required an acute airway intervention at presentation; 3 patients eventually underwent tracheotomy for respiratory decompensation. Six patients had a prior diagnosis of asthma, and this was determined to contribute to their respiratory status. Five patients were treated with botulinum toxin and 2 with flexible nasolaryngoscopic biofeedback, which improved the outcome. A review of the literature confirms a female predominance of patients presenting with paradoxical adduction and airway distress, often with a history of asthma and psychopathology. Our experience with botulinum toxin and biofeedback suggests that these procedures are viable treatment options in the management of patients with this disorder.  相似文献   
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This preliminary retrospective study of 19 female patients and 22male patients with unilateral recurrent nerve lesions demonstrated the promise of objective measurements in predicting the need for surgery, the efficacy of voice therapy in ameliorating vocal symptoms, and the effects of therapy in conjunction with surgery. Sixty-eight percent (68%) of the female patients and 64% of the male patients did not elect to have surgery. Outcome satisfaction of nonsurgical and surgical patients appeared to be similar. The data from this study support the importance of preoperative therapy for patients with unilateral vocal fold paralysis.  相似文献   
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徐启俊  方江敏  谈震 《低温与超导》2011,39(11):33-37,71
文中运用工程模拟计算软件ASPEN PLUS对LNG船用蒸发气体(BOG)再液化装置工艺流程进行了较全面和深入的模拟计算.通过对模拟计算结果进行分析,得到用丙烯预冷的再液化工艺流程中的主要工艺设备运行参数:海水冷凝器冷凝温度、BOG压缩机出口压力、丙烯压缩机出口压力以及混合制冷剂压缩机出口压力对再液化效率和能耗有着不同...  相似文献   
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The focus of this presentation is on the development of a neuroanatomical model underlying laryngeal function. It is suggested that the model can be used as an aid to the understanding of the structures that support phonatory output as well as a basis for physiological research on the nature of voice production. Although the postulates are anatomically grounded, the approach utilized also shows that there is ongoing feedback during phonation and, hence, that both the motor and sensory (neural) networks are coordinated during any phonatory activity. Additionally, the discussion serves to review the neuroanatomical interface between respiration and voice.  相似文献   
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近红外光谱(NIRS)广泛应用于生产过程分析与监测,常需事先建立定量校正或定性判别模型,并需在生产条件变化后调整模型,使用较复杂。本文从相异度和相似度两个对立互补的角度,提出自适应移动窗口标准差法和过程光谱相似度法,并以此为基础建立一种针对生产过程的无需校正模型的简易光谱在线监测方法。论文以中药柱层析过程为例,对监测过程作NIRS自适应移动窗口标准差趋势图和过程光谱相似度趋势图,并通过HPLC离线分析所得的多指标成分含量变化趋势图进行对比验证,发现可用于工艺状况实时监测,指导收集起点、终点、溶液相变点的判断,表明论文提出的方法合理可行。该方法亦可用于紫外/可见、红外、拉曼、荧光等光谱及色谱、质谱等其他过程分析技术。  相似文献   
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In clinical practice, segmentation and quantitative evaluation of target objects in pathological images provide valuable information for histopathological analysis, which is of great significance to auxiliary diagnosis and subsequent treatment. However, due to the dense distribution of cells and great morphological similarities between the cancer cells and normal cells, there are some challenges such as difficulty in feature extraction and unclear segmentation boundaries in the segmentation task of pathological images. At the same time, the traditional image segmentation methods are time-consuming and labor-intensive. They can only extract low level manual features, and the expression ability of deep discrimination features is insufficient, resulting in limited performance of traditional methods. Meanwhile, previous deep learning algorithms still suffer from two significant problems. Firstly, most networks ignore pixels that are difficult to segment, such as the boundaries of targets, which is particularly important for accurate segmentation. In addition, the problem of inconsistent semantic levels between different features are not solved, leading to low training efficiency. To address the above-mentioned problems, an end-to-end histopathological image segmentation network called Boundary Perception Network (BPNet) is proposed for improving the segmentation accuracy of histopathological images. Based on encoder-decoder structure, the encoder performs the convolutional downsampling operation to extract the feature information of the image through the Convolutional Neural Network (CNN). And the encoding process uses the feature encoder based on the EfficientNet-B4 network which is specifically used for pathological image segmentation. The decoder mainly consists of decooding blocks, Boundary Perception Module (BPM) and Adative Shuffle Channel Attention Moudule (ASCAM). In detail, the decoding block performs deconvolution operation to complete the decoding process of the feature information. Then, the BPM in the decoder stage aims to strengthen the ability of mining for difficult segmentation regions, so that the network focuses on the higher uncertainty as well as more complex edge regions, achieving feature complementarity and precision prediction results. For implementation, the BPM extracts the edge from the decoder output of each layer, and superimposes the edge information onto the encoded feature to strengthen the boundary feature information extracted from pathological images, outputting the enhanced edge perception feature map. Subsequently, the ASCAM is an improved chanel attention moudule which is used to make up the semantic gap between different levels of features, extrated by encoder, decoder and BPM, so as to further strengthens the feature understanding ability of the BPNet. This module exploits adaptive kernel size one-dimensional convolusion to capture the interactive information of local channels, at the same time ensures the efficiency and effectiveness of the training process. The obtained channel attention coefficient is multiplied by the module input feature layer to obtain the fusion feature, helping effectively learn the channel interaction information between features to improve the feature representation ability. Furthermore, a joint loss function based on structure and boundary is designed to optimize the targeting and detail processing capabilities of this method, achieving the better segmentation result of pathological images. Experiments are carried out on the Gland segmentation (GlaS) and MoNuSeg dataset, respectively. Both of the two datasets are devided into 4∶1 for training and validation. At the same time, in order to make up for the overfitting caused by the lack of training data, two kinds of online data enhancement methods of horizontal flipping and vertical flipping were carried out on the training set data in the experiment. And the four evaluation index, the Dice coefficient score, Intersection Over Union (IoU), Accuracy (ACC) and Precision (PRE), are used to evaluate the performance of this method propsed in this paper. The Dice coefficient score of the proposed method is 92.21% and 81.18%, the IoU is 85.55% and 68.34%, the ACC is 92.14% and 92.50%, the PRE is 92.07% and 75.46% on the GlaS and MoNuSeg datasets, respectively. Compared with the previous classical methods, such as U-Net, UNet++, MultiResUNet, TransUNet, UCTransNet and so on, the BPNet proposed gets the best segmentation result, especially retains more details in the segmentation boundary. Moreover, ablation experiments are carried out on the same two datasets for indicating the impacts of BPM and ASCAM. The results shows that the proposed BPM significantly optimizes the segmentation effect of the network for the edge, as well as the ASCAM makes up the semantic gap between features at different levels and further strengthens the feature understanding ability of the network. In conclusion, the BPNet proposed in this paper exploits BPM to generate edge enhancement feature maps, and uses ASCAM to seize crucial features. Finally, a joint loss function is used to capture the information of features at different levels in the output layer to achieve optimal segmentation performance. The experimental results have demonstrated that the effectiveness of each part of proposed method in the segmentation task of pathological images.  相似文献   
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