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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.

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.

Let $A$ be a finite dimensional hereditary algebra over an algebraically closed field and $A^{(m)}$ the $m$-replicated algebra of $A$. We prove that the representation dimension of $A^{(m)}$ is at most three, and that the dominant dimension of $A^{(m)}$ is at least $m$., Comment: 13 pages

Let $A$ be a hereditary artin algebra and $A^{(m)}$ be the $m$-replicated algebra of $A$. We investigate the possibilities for the global dimensions of the endomorphism algebras of generator-cogenerators over $A^{(m)}$., Comment: 17 pages, accepted for publication in Comm.Algebra

Let $H$ be a hereditary algebra of Dynkin type $D_n$ over a field $k$ and $\mathscr{C}_H$ be the cluster category of $H$. Assume that $n\geq 5$ and that $T$ and $T'$ are tilting objects in $\mathscr{C}_H$. We prove that the cluster-tilted algebra $\Gamma=\mathrm{End}_{\mathscr{C}_H}(T)^{\rm op}$ is isomorphic to $\Gamma'=\mathrm{End}_{\mathscr{C}_H}(T')^{\rm op}$ if and only if $T=\tau^iT'$ or $T=\sigma\tau^jT'$ for some integers $i$ and $j$, where $\tau$ is the Auslander-Reiten translation and $\sigma$ is the automorphism of $\mathscr{C}_H$ defined in section 4., Comment: 21 pages, 13 figures, accepted for publication in Comm.Algebra