Entangled Three Qutrit Coherent States and Localizable Entanglement

Choosing I-concurrence as the measure of bipartite entanglement and using yon Neumann projective local measurements, localizable entanglement （LE） in a three-qutrit system is studied. A superposition of the qutrit-coherent- state of this system is considered ant its LE is obtained and analyzed as a function of the relevant parameters. It is observed that one may achieve the maximal entanglement or no entanglement at all, depending on the choice of the parameters involved.

Oncogenic role of p21 in hepatocarcinogenesis suggests a new treatment strategy

A well-known tumor suppressor, p21, acts parado-xically by promoting tumor growth in some cellular conditions. These conflicting functions have been demonstrated in association with the HBx gene and in hepatocarcinogenesis. The molecular behavior of p21 depends on its subcellular localization. Nuclear p21 may inhibit cell proliferation and be proapoptotic, while cytoplasmic p21 may have oncogenic and anti-apoptotic functions. Because most typical tumor suppressive proteins also have different effects according to subcellular localization, elucidating the regulatory mechanisms underlying nucleo-cytoplasmic transport of these proteins would be significant and may lead to a new strategy for anti-hepatocellular carcinoma(HCC) therapy. Chromosome region maintenance 1(CRM1) is a major nuclear export receptor involved in transport of tumor suppressors from nucleus to cytoplasm. Expression of CRM1 is enhanced in a variety of malignancies and in vitro studies have shown the efficacy of specific inhibition of CRM1 against cancer cell lines. Interestingly, interferon may keep p21 in the nucleus; this is one of the mechanisms of its anti-hepatocarcinogenic function. Here we review the oncogenic property of p21, which depends on its subcellular localization, and discuss the rationale underlying a new strategy for HCC treatment and prevention.

A cluster positioning architecture and relative positioning algorithm based on pigeon fock bionics

Unmanned clusters can realize collaborative work,fexible confguration,and efcient operation,which has become an important development trend of unmanned platforms.Cluster positioning is important for ensuring the normal operation of unmanned clusters.The existing solutions have some problems such as requiring external system assistance,high system complexity,poor architecture scalability,and accumulation of positioning errors over time.Without the aid of the information outside the cluster,we plan to construct the relative position relationship with north alignment to adopt formation control and achieve robust cluster relative positioning.Based on the idea of bionics,this paper proposes a cluster robust hierarchical positioning architecture by analyzing the autonomous behavior of pigeon focks.We divide the clusters into follower clusters,core clusters,and leader nodes,which can realize fexible networking and cluster expansion.Aiming at the core cluster that is the most critical to relative positioning in the architecture,we propose a cluster relative positioning algorithm based on spatiotemporal correlation information.With the design idea of low cost and large-scale application,the algorithm uses intra-cluster ranging and the inertial navigation motion vector to construct the positioning equation and solves it through the Multidimensional Scaling(MDS)and Multiple Objective Particle Swarm Optimization(MOPSO)algorithms.The cluster formation is abstracted as a mixed direction-distance graph and the graph rigidity theory is used to analyze localizability conditions of the algorithm.We designed the cluster positioning simulation software and conducted localizability tests and positioning accuracy tests in diferent scenarios.Compared with the relative positioning algorithm based on Extended Kalman Filter(EKF),the algorithm proposed in this paper has more relaxed positioning conditions and can adapt to a variety of scenarios.It also has higher relative positioning accuracy,and the error does not accumulate over time.

Location,Localization,and Localizability

Location-aware technology spawns numerous unforeseen pervasive applications in a wide range of living, pro- duction, commence, and public services. This article provides an overview of the location, localization, and localizability issues of wireless ad-hoc and sensor networks. Making data geographically meaningful, location information is essential for many applications, and it deeply aids a number of network functions, such as network routing, topology control, coverage, boundary detection, clustering, etc. We investigate a large body of existing localization approaches with focuses on error control and network localizability, the two rising aspects that attract significant research interests in recent years. Error control aims to alleviate the negative impact of noisy ranging measurement and the error accumulation effect during coope- rative localization process. Network localizability provides theoretical analysis on the performance of localization approaches, providing guidance on network configuration and adjustment. We emphasize the basic principles of localization to under- stand the state-of-the-art and to address directions of future research in the new and largely open areas of location-aware technologies.

Passive-Event-Assisted Approach for the Localizability of Large-Scale Randomly Deployed Wireless Sensor Network

Localizability in large-scale, randomly deployed Wireless Sensor Networks(WSNs) is a classic but challenging issue. To become localizable, WSNs normally require extensive adjustments or additional mobile nodes. To address this issue, we utilize occasional passive events to ease the burden of localization-oriented network adjustment. We prove the sufficient condition for node and network localizability and design corresponding algorithms to minimize the number of nodes for adjustment. The upper bound of the number of adjusted nodes is limited to the number of articulation nodes in a connected graph. The results of extensive simulations show that our approach greatly reduces the cost required for network adjustment and can thus provide better support for the localization of large-scale sparse networks than other approaches.