Comparison of Simultaneous Na Lidar and MesosphericTemperature Mapper Measurements and the Effects Of Tides On the Emission Layer Heights

A detailed comparative study of two new mesospheric temperature data sets, measured by different remote-sensing techniques, has been performed as part of a long-term investigation of low-latitude mesospheric dynamics. Coincident observations using the University of Illinois Na wind/temperature lidar and the Utah State University CEDAR Mesospheric Temperature Mapper (MTM) were conducted from the summit of Haleakala Crater, Maui, Hawaii (20.8°N, 156.2°W, ∼3000 m) as part of the Maui-MALT program. High-quality joint measurements were obtained during four lidar campaign periods, and 16 nights of data, spanning the interval January 2002 to October 2003, are presented here as example observations during each season. The Na lidar was coupled to the Air Force 3.7 m diameter steerable telescope providing exceptional quality temperature (and wind) data spanning the altitude range ∼80–105 km. At the same time the MTM sequentially sampled the NIR OH (6,2) Meinel band and the O2 (0,1) Atmospheric band nightglow emissions to determine the height-weighted mesospheric temperature at two nominal altitudes within this region. Comparison of these two nocturnal data sets shows exceptionally good agreement on a point-to-point, as well as a nightly mean basis, especially when allowances were made for physically reasonable changes in height during the course of the night for each of the nightglow layers. This analysis yields mean nocturnal altitudes of 88.6 km with a nocturnal variability of ±3.0 km for the OH M (6,2) band emission layer and 94.4 km for the O2 (0,1) Atmospheric band mean altitude with a nocturnal variability of ±4.2 km. These results are in excellent accord with previous rocket, satellite and ground-based observations and further establish the validity of these two complementary measurement techniques. Furthermore, analysis of the computed height changes inferred from this study indicates a systematic decrease in altitude of both emission layers by up to several kilometers, whenever the lidar data showed evidence of strong diurnal or semidiurnal tidal forcing. The apparent downward trend in altitude was found to track the phase of the prevailing tidal motion providing new evidence for the effects of tides (or long-period gravity waves) on the height variability of the nightglow layers

Seamless handover in software-defined satellite networking

Satellites have largely been designed as application specific and isolated for the past decades. Though with certain benefits, it might lead to resource under utilization and limited satellite applications. As an emerging networking technology, software-defined networking (SDN) has recently been introduced into satellite networks. In this letter, we propose a software defined satellite networking (SDSN) architecture, which simplifies networking among versatile satellites and enables new protocols to be easily tested and deployed. Particularly, we propose a seamless handover mechanism based on SDSN, and conduct physical layer simulation, which shows significant improvement over the existing hard handover and hybrid handover mechanisms in terms of handover latency, throughput and quality of experience of users

Multi-slice ptychography with large numerical aperture multilayer Laue lenses

The highly convergent x-ray beam focused by multilayer Laue lenses with large numerical apertures is used as a three-dimensional (3D) probe to image layered structures with an axial separation larger than the depth of focus. Instead of collecting weakly scattered high-spatial-frequency signals, the depth-resolving power is provided purely by the intense central cone diverged from the focused beam. Using the multi-slice ptychography method combined with the on-the-fly scan scheme, two layers of nanoparticles separated by 10 μm are successfully reconstructed with 8.1 nm lateral resolution and with a dwell time as low as 0.05 s per scan point. This approach obtains high-resolution images with extended depth of field, which paves the way for multi-slice ptychography as a high throughput technique for high-resolution 3D imaging of thick samples

A sparse Bayesian learning method for structural equation model-based gene regulatory network inference

Gene regulatory networks (GRNs) are underlying networks identified by interactive relationships between genes. Reconstructing GRNs from massive genetic data is important for understanding gene functions and biological mechanism, and can provide effective service for medical treatment and genetic research. A series of artificial intelligence based methods have been proposed to infer GRNs from both gene expression data and genetic perturbations. The accuracy of such algorithms can be better than those models that just consider gene expression data. A structural equation model (SEM), which provides a systematic framework integrating both types of gene data conveniently, is a commonly used model for GRN inference. Considering the sparsity of GRNs, in this paper, we develop a novel sparse Bayesian inference algorithm based on Normal-Equation-Gamma (NEG) type hierarchical prior (BaNEG) to infer GRNs modeled with SEMs more accurately. First, we reparameterize an SEM as a linear type model by integrating the endogenous and exogenous variables; Then, a Bayesian adaptive lasso with a three-level NEG prior is applied to deduce the corresponding posterior mode and estimate the parameters. Simulations on synthetic data are run to compare the performance of BaNEG to some state-of-the-art algorithms, the results demonstrate that the proposed algorithm visibly outperforms the others. What’s more, BaNEG is applied to infer underlying GRNs from a real data set composed of 47 yeast genes from Saccharomyces cerevisiae to discover potential relationships between genes

STAR:Spatio-temporal taxonomy-aware tag recommendation for citizen complaints

In modern cities, complaining has become an important way for citizens to report emerging urban issues to governments for quick response. For ease of retrieval and handling, government officials usually organize citizen complaints by manually assigning tags to them, which is inefficient and cannot always guarantee the quality of assigned tags. This work attempts to solve this problem by recommending tags for citizen complaints. Although there exist many studies on tag recommendation for textual content, few of them consider two characteristics of citizen complaints, i.e., the spatio-temporal correlations and the taxonomy of candidate tags. In this paper, we propose a novel Spatio-Temporal Taxonomy-Aware Recommendation model (STAR), to recommend tags for citizen complaints by jointly incorporating spatio-temporal information of complaints and the taxonomy of candidate tags. Specifically, STAR first exploits two parallel channels to learn representations for textual and spatio-temporal information. To effectively leverage the taxonomy of tags, we design chained neural networks that gradually refine the representations and perform hierarchical recommendation under a novel taxonomy constraint. A fusion module is further proposed to adaptively integrate contributions of textual and spatio-temporal information in a tag-specific manner. We conduct extensive experiments on a real-world dataset and demonstrate that STAR significantly performs better than state-of-the-art methods. The effectiveness of key components in our model is also verified through ablation studies

Resource allocation and power control to maximize the overall system survival time for mobile cells with a D2D underlay

The limited battery life of user equipment (UE) is always one of the key concerns of mobile users and a critical factor that could limit device-to-device (D2D) communications. In this letter, considering that UEs may have different residual battery energy levels, we define the overall system survival time as the minimal expected battery lifetime of all transmitting UEs in a cell. We then propose to maximize the overall system survival time by jointly optimizing the resource allocation and power control (RAPC) D2D links and conventional cellular links. Subject to the transmission rate requirement of each link, the joint optimization problem is formulated as a mixed integer non-linear programming problem, which is solved by a game theory-based distributed approach. Simulation results demonstrate that our game theory-based RAPC approach can enormously prolong the overall system survival time as compared with existing RAPC approaches

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

Trace doping of multiple elements enables stable battery cycling of LiCoO2 at 4.6 V

LiCoO2 is a dominant cathode material for lithium-ion (Li-ion) batteries due to its high volumetric energy density, which could potentially be further improved by charging to high voltages. However, practical adoption of high-voltage charging is hindered by LiCoO2’s structural instability at the deeply delithiated state and the associated safety concerns. Here, we achieve stable cycling of LiCoO2 at 4.6 V (versus Li/Li+) through trace Ti–Mg–Al co-doping. Using state-of-the-art synchrotron X-ray imaging and spectroscopic techniques, we report the incorporation of Mg and Al into the LiCoO2 lattice, which inhibits the undesired phase transition at voltages above 4.5 V. We also show that, even in trace amounts, Ti segregates significantly at grain boundaries and on the surface, modifying the microstructure of the particles while stabilizing the surface oxygen at high voltages. These dopants contribute through different mechanisms and synergistically promote the cycle stability of LiCoO2 at 4.6 V