Earticle

Web-based specialized retrieval systems for scientific fields extremely restrict the expression for user's information requests. Therefore the process of information content analysis and that of the information acquisition become inconsistent. In this paper, we apply the fuzzy retrieval model to solve the high time complexity of the retrieval system by constructing a reduced term set for the term's relatively important degree. We also perform a cluster retrieval to reflect the user's query exactly through the similarity relation matrix satisfying the characteristics of the fuzzy compatibility relation. This paper proves the performance of a proposed re-ranking model based on the union of similarity of the fuzzy retrieval model and the document cluster retrieval model.

Purpose: This study attempted using a descriptive survey to elucidate the influence of anxiety and dyadic adjustment on maternal–fetal attachment in high-risk pregnant women. Methods: The data used in this study were collected from March 3rd, 2015, to March 30th, 2015, and the participants were 118 pregnant women including those undergoing prenatal tests and those admitted to a delivery room in the obstetrics outpatient center of 3 university hospitals located in B, D, and Y after diagnosis with high-risk pregnancy during 20–38 weeks of gestation. Collected data were analyzed using frequency, percentage, mean, standard deviation, t-test, ANOVA, Pearson’s correlation, and stepwise regression analysis using the IBM SPSS 22.0 program. Results: The level of maternal–fetal attachment according to participants’ general and obstetric characteristics showed significant differences in history of childbirth, prenatal tests, and planned pregnancy. The factors that influenced maternal–fetal attachment were history of childbirth, prenatal tests, anxiety, and dyadic adjustment. Lower anxiety and high dyadic adjustment of high-risk pregnant women led to a high maternal–fetal attachment. Among them, anxiety was the factor with the greatest impact, explaining 20.5%. Conclusion: This study presents the need for development and application of prenatal nursing intervention to enhance maternal–fetal attachment by lowering anxiety through prenatal care in high-risk pregnant women and improving dyadic adjustment.

This paper describes the design of smart learning in collaborative environment based on mobile device. The components of the smart learning environment are mobile microprocessors, platform module, wireless network infra, sensors. The devices used include smartphone, and tablet PCs. In our research, we have designed possibilities to collaborative learning in smart learning with wireless networks and mobile devices. Smart learning is not only addresses the flexibility of mobile technologies, but also achieves collaborative learning in real time. This paper is to become a more capable student learning environment so that student can get student’s mobile learning done more efficiently. A study of a smart learning combines the advantages of a collaborative learning environment with the benefits of intelligent feedback and the flexibility of mobile devices. We focus on three aspects, mobile environment, collaborative learning in virtual environment, and smart learning.

This paper addresses the asymptotic diversity performance of Alamouti transmit diversity technique especially in time-selective fading channels. In particular, a linear quasi-maximum-likelihood (QML) decoding method is employed for the Alamouti transmit diversity system to solve the error floor problem induced by the conventional linear ML decoding method. By judiciously utilizing the derived asymptotic closed-form formula of symbol pairwise error rate (SPER), it is theoretically verified that the asymptotic diversity orders achieved by the QML decoding algorithm become 2 and 1 in quasi-static and time-selective fading channels, respectively.