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To explore the influence of the parturients' mental factors on the delivery mode and postpartum hemorrhage and the nursing intervention countermeasures, the research randomly divided the expectant primiparae hospitalized in our hospital during February 2012–February 2013 into the intervention group and the control group. Wherein, the control group received routine nursing, while the intervention group received nursing intervention based on the routine nursing, to compare the postpartum hemorrhage and satisfaction of the two groups of parturients. Result: 36 of 112 parturients suffered from dysphoria, accounting for 32.14%; 37 parturSients suffered from depression, accounting for 33.04%. The parturients' hemorrhage incidence, postpartum amount of bleeding within 2 h and postpartum amount of bleeding within 24 h all significantly declined compared with those of the control group (x2/t = 3.98, 4.02, 4.96 p < 0.05). The cesarean rate of the control group (7.14%, 4/56) was significantly higher than that of the control group (16.07%, 9/56) (x2 = 4.02 p < 0.05). The postpartum satisfaction of the patients in the intervention group was apparently higher than that of the control group (x2 = 4.26 p < 0.05). Conclusion: The nursing intervention can obviously reduce the incidence of postpartum hemorrhage and improve postpartum satisfaction, so it is worth popularizing.

A robot belt grinding system provides promising prospects for relieving hand grinders from their noisy work environment, as well as for improving machining accuracy and product consistency. However, for a manufacturing system with a flexible grinder, controlling the robot to perform precise material removal from free-form surfaces is a challenge. In the belt grinding process, material removal is related to a variety of factors, such as workpiece shape, contact force, and robot velocity. Some factors of the grinding process, such as belt wear, are time-variant. To achieve the desired removal in the grinding process, an intelligent control method for the industrial robot is proposed in this paper. First, an adaptive grinding process model that can track discontinuous changes in working conditions is constructed to precisely predict material removal in accordance with in situ measurement data. With incorporated prior knowledge, the method considerably improves model accuracy, which worsens when new samples from an in situ measurement are insufficient or are unevenly distributed under new working conditions. After this, an online trajectory generation method for the robot control parameters is proposed. By calculating the optimal control parameters in real time, the control transition process is shortened and its negative effect on grinding quality is reduced. Finally, the preliminary grinding experiments validate the workability and effectiveness of the proposed control method.

In Xinjiang, China, esophageal carcinoma has a high incidence in Kazak and Uighur populations. MicroRNA (miR)-21 and miR-375 are related to esophageal carcinoma. This study thus investigated their potencials in early diagnosis and prognosis in Kazak and Uighur populations, to provide evidences for serum markers of esophageal cancer. A total of 126 Kazak or Uighur esophageal cancer patients were enrolled as the disease group, along with 86 local Han patients as disease control cohort, and 80 healthy Kazak or Uighur individuals. MiRNA expression was detected by in situ hybridization in tissues and by qRT-PCR in serum. ROC approach was used to evaluate the diagnostic value of miRNA on esophageal carcinoma. Cox analysis was performed to screen factors governing prognosis. MiR-21 level was significantly elevated in both tissue and serum samples of esophageal cancer patients, while miR-375 was down-regulated. Such difference was more potent in disease group compared to disease control group. MiR expression was correlated with infiltration depth, TNM stage, vascular invasion, and lymph node metastasis. Elevated expression of miR-21 reduced the sensitivity of radio-therapy, and increased recurrence frequency. The diagnostic value of single assay for miR-21 or miR-375 was lower than the combined assay (AUC=0.812 or 0.739 vs. 0.858). They also affected patient prognosis (OR=1.53 or 0.652). MiR-21 and miR-375 presented abnormal expression in Kazak or Uighur esophageal carcinoma patients and were independent factors affecting prognosis. The combined assay of miR-21 and miR-375 may help to make early diagnosis of esophageal cancer.

A robot belt grinding system has a good prospect for releasing hand grinders from their dirty and noisy work environment. However, as a kind of manufacturing system with a flexible grinder, it is a challenge to model its processes and control grinding removal precisely for free-formed surfaces. In the belt grinding process, material removal is related to a variety of factors, such as workpiece shape, contact force, and robot velocity. Some factors of the grinding process, such as belt wear, are time variant. In order to control material removal in the robot grinding process, an effective approach is to build a grinding process model that can track changes in the working condition and predict material removal precisely. In this paper, an adaptive modeling method based on statistic machine learning is proposed. The major idea is to build an initial model based on support vector regression using historical grinding data serving as training samples. Afterward, the trained model is modified according to in situ measurement data. Robot control parameters can then be calculated using the grinding process model. The results of the blade grinding experiments demonstrate that this approach is workable and effective.