Magnetodielectric effect of Bi6Fe2Ti3O18 film under an ultra-low magnetic field

Good quality and fine grain Bi6Fe2Ti3O18 magnetic ferroelectric films with single-phase layered perovskite structure have been successfully prepared via metal organic decomposition (MOD) method. Results of low-temperature magnetocapacitance measurements reveal that an ultra-low magnetic field of 10 Oe can produce a nontrivial magnetodielectric (MD) response in zero-field-cooling condition, and the relative variation of dielectric constants in magnetic field is positive, i.e., MD=0.05, when T<55K, but negative with a maximum of MD=-0.14 when 55K<T<190K. The magnetodielectric effect appears a sign change at 55K, which is due to transition from antiferromagnetic to weak ferromagnetic; and vanishes abruptly around 190K, which is thought to be associated with order-disorder transition of iron ion at B site of perovskite structures. The ultra-low-field magnetodielectric behaviour of Bi6Fe2Ti3O18 film has been discussed in the light of quasi-two-dimension unique nature of local spin order in ferroelectric film. Our results allow expectation on low-cost applications of detectors and switches for extremely weak magnetic fields in a wide temperature range 55K-190K.Comment: 10 pages 4 figures, planned to submit to J. Phys.: Condensed Matte

Observation of Fermi-energy dependent unitary impurity resonances in a strong topological insulator Bi_2Se_3 with scanning tunneling spectroscopy

Scanning tunneling spectroscopic studies of Bi_2Se_3 epitaxial films on Si (111) substrates reveal highly localized unitary impurity resonances associated with non-magnetic quantum impurities. The strength of the resonances depends on the energy difference between the Fermi level (E_F) and the Dirac point (E_D) and diverges as E_F approaches E_D. The Dirac-cone surface state of the host recovers within ~ 2Å spatial distance from impurities, suggesting robust topological protection of the surface state of topological insulators against high-density impurities that preserve time reversal symmetry

Target recognition for coastal surveillance based on radar images and generalised Bayesian inference

For coastal surveillance, this study proposes a novel approach to identify moving vessels from radar images with the use of a generalised Bayesian inference technique, namely the evidential reasoning (ER) rule. First of all, the likelihood information about radar blips is obtained in terms of the velocity, direction, and shape attributes of the verified samples. Then, it is transformed to be multiple pieces of evidence, which are formulated as generalised belief distributions representing the probabilistic relationships between the blip's states of authenticity and the values of its attributes. Subsequently, the ER rule is used to combine these pieces of evidence, taking into account their corresponding reliabilities and weights. Furthermore, based on different objectives and verified samples, weight coefficients can be trained with a non-linear optimisation model. Finally, two field tests of identifying moving vessels from radar images have been conducted to validate the effectiveness and flexibility of the proposed approach

An accuracy measurement method for star trackers based on direct astronomic observation.

Star tracker is one of the most promising optical attitude measurement devices and it is widely used in spacecraft for its high accuracy. However, how to realize and verify such an accuracy remains a crucial but unsolved issue until now. The authenticity of the accuracy measurement method of a star tracker will eventually determine the satellite performance. A new and robust accuracy measurement method for a star tracker based on the direct astronomical observation is proposed here. In comparison with the conventional method with simulated stars, this method utilizes real navigation stars as observation targets which makes the measurement results more authoritative and authentic. Transformations between different coordinate systems are conducted on the account of the precision movements of the Earth, and the error curves of directional vectors are obtained along the three axes. Based on error analysis and accuracy definitions, a three-axis accuracy evaluation criterion has been proposed in this paper, which could determine pointing and rolling accuracy of a star tracker directly. Experimental measurements confirm that this method is effective and convenient to implement. Such a measurement environment is close to the in-orbit conditions and it can satisfy the stringent requirement for high-accuracy star trackers.This work was financially supported by the National High Technology Research and Development Program of China (863 Program) (No. 2012AA121503), National Natural Science Foundation of China (No. 61377012 and No. 51522505) and the China Postdoctoral Science Foundation (No. 2015M570091).This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/srep2259

A Flexsim-based Optimization for the Operation Process of Cold-Chain Logistics Distribution Centre

AbstractWith people’s increasing concern about food safety, cold-chain logistics distribution centre is playing an important role in preventing food from going bad. Now cold-chain logistics distribution centres have the problems of too much transportation, low degree of automation, unreasonable layout planning, complex distribution process etc. It is important to solve these problems in order to achieve efficient distribution. Firstly the modeling and simulation for the operation process of a fruits and vegetables cold-chain logistics distribution centre by using Flexsim software is realized. Then the paper analyses the preliminary output data and finds out the bottleneck and idle resources. Finally this paper makes adjustments for the system to get a better result which hopes to give a reference for the modeling and simulation for the operation process of other cold-chain logistics distribution centres

Computation offloading and resource allocation for wireless powered mobile edge computing with latency constraint

In this letter, we consider a multi-user wireless powered mobile edge computing (MEC) system, in which a base station (BS) integrated with an MEC server transfers energy to wireless devices (WDs) as an incentive to encourage them to offload computing tasks to the MEC server. We formulate an optimization problem to contemporaneously maximize the data utility and minimize the energy consumption of the operator under the offloaded delay constraint, by jointly controlling wireless-power allocation at the BS as well as offloaded data size and power allocation at the WDs. To solve this problem, the offloaded delay constraint is first transformed into an offloaded data rate constraint. Then an iterative algorithm is designed to obtain the optimal offloaded data size and power allocation at the WDs by using Lagrangian dual method. The results are applied to derive the optimal wireless-power allocation at the BS. Finally, simulation results show that our algorithm outperforms existing schemes in terms of operator’s reward

Trajectory optimization and resource allocation for UAV base stations under in-band backhaul constraint

The application of unmanned aerial vehicles (UAVs) to emerging communication systems has attracted a lot of research interests due to the advantages of UAVs, such as high mobility, flexible deployment, and cost-effectiveness. The UAV-carried base stations (UAV-BS) can provide on-demand service to users in temporary or emergency events. However, how to optimize the communication performance of a UAV-BS with a limited-bandwidth wireless backhaul is still a challenge. This paper focuses on improving the spectrum efficiency of a UAV-BS while guaranteeing user fairness under in-band backhaul constraint. We propose to maximize the minimum user rate among all the users served by the UAV-BS by jointly optimizing the allocation of bandwidth and transmit power, as well as the trajectory of the UAV-BS. As the formulated problem is non-convex, we propose an efficient algorithm to solve it suboptimally based on the alternating optimization and successive convex optimization methods. Computer simulation results show that the proposed algorithm achieves a significantly higher minimum user rate than the benchmark schemes

Graphene Oxide Ameliorates the Cognitive Impairment Through Inhibiting PI3K/Akt/mTOR Pathway to Induce Autophagy in AD Mouse Model

Alzheimer’s disease (AD) is a neurodegenerative disease of the central nervous system characterised by cognitive impairment. Its major pathological feature is the deposition of β-amyloid (Aβ) peptide, which triggers a series of pathological cascades. Autophagy is a main pathway to eliminate abnormal aggregated proteins, and increasing autophagy represents a plausible treatment strategy against relative overproduction of neurotoxic Aβ. Graphene oxide (GO) is an emerging carbon-based nanomaterial. As a derivative of graphene with neuroprotective effects, it can effectively increase the clearance of abnormally aggregated protein. In this article, we investigated the protective function of GO in an AD mouse model. GO (30 mg/kg, intraperitoneal) was administered for 2 weeks. The results of the Morris water maze test and the novel object recognition test suggested that GO ameliorated learning and memory impairments in 5xFAD mice. The long-term potentiation and depotentiation from the perforant path to the dentate gyrus in the hippocampus were increased with GO treatment in 5xFAD mice. Furthermore, GO upregulated the expression of synapse-related proteins and increased the cell density in the hippocampus. Our results showed that GO up-regulated LC3II/LC3I and Beclin-1 and decreased p62 protein levels in 5xFAD mice. In addition, GO downregulated the PI3K/Akt/mTOR signalling pathway to induce autophagy. These results have revealed the protective potential of GO in AD