Clinical Effect of Yunkang Granule Combined with Low Dose Aspirin in the Treatment of Unexplained Recurrent Abortion and Its Influence on Pregnancy Outcome

Purpose: To study the clinical effect of Yunkang Granule combined with low dose aspirin in the treatment of unexplained recurrent abortion (URSA) and its impact on pregnancy outcome. Methods: Seventy-two patients with URSA were randomly divided into a control group and an experimental group. The control group was given conventional treatment, and the experimental group was given pregnancy-kang granules combined with low-dose aspirin. The clinical efficacy of the two treatments and their effects on hormone levels, coagulation function indexes and pregnancy outcome were compared. Results: After medication, the levels of β-HCG, P and E2 in 2 groups were higher than before medication, and the levels of β-HCG, P and E2 in observation group were higher than control group, the difference was statistically significant (P < 0.05). Compared with before medication, the levels of coagulation function indexes PT, TT and APTT were increased in both groups after medication, while the levels of FIB were decreased. The levels of coagulation function indexes PT, TT, APTT and FIB in observation group were better than those in control group after medication, and the difference was statistically significant (P < 0.05). After treatment, the total effective rate of the experimental group was significantly increased, and the pregnancy outcome was significantly improved. Conclusion: The combination of Yunkang granule and low-dose aspirin in the treatment of URSA has obvious curative effect, can significantly improve the pregnancy outcome, and is worthy of clinical promotion

Brief on Discounting in the Context of Climate Change Economics

human development, climate change

An Exploration of Knowledge Integration: A Comprehensive View of Media Characteristics and Integration Capability

With the increasing tendency of information technology (IT) applications, knowledge integration in organization has emerged as an important research topic for both practitioners and researchers, reflecting the magnitude and impact of knowledge resource in distributed environments. Practically, data, information, and knowledge are transmitted via various media, and integration capability is the antecedent of knowledge integration in organizations. However, few literatures research knowledge integration from the comprehensive perspective of transmission media and integration capability. As a complement to previous research, this study proposes a comprehensive theoretical model of knowledge integration (KIM) by incorporating valuable perspectives of media characteristics and organizational capability. This research model provides a new lens for knowledge integration based on transmission media in organizations by integrating relevant research streams. Furthermore, this robust and new model, which has been applied to a knowledge integration practice of e-commerce platform, can be used to explain the most knowledge integration phenomenon in contemporary society

Clustering of Web Users Using Session-based Similarity Measures

One important research topic in web usage mining is the clustering of web users based on their common properties. Informative knowledge obtained from web user clusters were used for many applications, such as the prefetching of pages between web clients and proxies. This paper presents an approach for measuring similarity of interests among web users from their past access behaviors. The similarity measures are based on the user sessions extracted from the user\u27s access logs. A multi-level scheme for clustering a large number of web users is proposed, as an extension to the method proposed in our previous work (2001). Experiments were conducted and the results obtained show that our clustering method is capable of clustering web users with similar interest

Efficient Truss Maintenance in Evolving Networks

Truss was proposed to study social network data represented by graphs. A k-truss of a graph is a cohesive subgraph, in which each edge is contained in at least k-2 triangles within the subgraph. While truss has been demonstrated as superior to model the close relationship in social networks and efficient algorithms for finding trusses have been extensively studied, very little attention has been paid to truss maintenance. However, most social networks are evolving networks. It may be infeasible to recompute trusses from scratch from time to time in order to find the up-to-date $k$-trusses in the evolving networks. In this paper, we discuss how to maintain trusses in a graph with dynamic updates. We first discuss a set of properties on maintaining trusses, then propose algorithms on maintaining trusses on edge deletions and insertions, finally, we discuss truss index maintenance. We test the proposed techniques on real datasets. The experiment results show the promise of our work

The 1st International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA)

Motivation and Goals. With the explosive growth of online service platforms, increasing number of people and enterprises are doing everything online. In order for organizations, governments, and individuals to understand their users, and promote their products or services, it is necessary for them to analyse big data and recommend the media or online services in real time. Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. Though many scientific breakthroughs have been achieved, there are still tremendous challenges in developing effective and scalable recommendation systems for real-world industrial applications. Existing solutions focus on recommending items based on pre-set contexts, such as time, location, weather etc. The big data sizes and complex contextual information add further challenges to the deployment of advanced recommender systems. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics. In a broad sense, the objective of such a workshop is to present results of the research undertaken in the area of data driven context-aware recommender systems, as a fishow and tellfi occasion. To some extent, the workshop is an exercise in showcasing research activities and findings, rather than in and not of fiworkshoppingfi or holding group discussions on research. This orientation, and the large number of presentations which are being made, means that tight timelines have to be followed. An intensive series of presentations is made, the downside of which is that the time available for group discussion is limited

Delegating revocations and authorizations in collaborative business environments

Efficient collaboration allows organizations and individuals to improve the efficiency and quality of their business activities. Delegations, as a significant approach, may occur as workflow collaborations, supply chain collaborations, or collaborative commerce. Role-based delegation models have been used as flexible and efficient access management for collaborative business environments. Delegation revocations can provide significant functionalities for the models in business environments when the delegated roles or permissions are required to get back. However, problems may arise in the revocation process when one user delegates user U a role and another user delegates U a negative authorization of the role. This paper aims to analyse various role-based delegation revocation features through examples. Revocations are categorized in four dimensions: Dependency, Resilience, Propagation and Dominance. According to these dimensions, sixteen types of revocations exist for specific requests in collaborative business environments: DependentWeakLocalDelete, Dependent WeakLocalNegative, DependentWeakGlobalDelete, DependentWeakGlobalNegative, IndependentWeak LocalDelete, IndependentWeakLocalNegative, Inde pendentWeakGlobalDelete, IndependentWeakGlobal Negative, and so on. We present revocation delegating models, and then discuss user delegation authorization and the impact of revocation operations. Finally, comparisons with other related work are discussed