A new small satellite sunspot triggering recurrent standard- and blowout-coronal jets

In this paper,we report a detailed analysis of recurrent jets originated from a location with emerging, canceling and converging negative magnetic field at the east edge of NOAA active region AR11166 from 2011 March 09 to 10. The event presented several interesting features. First, a satellite sunspot appeared and collided with a pre-existing opposite polarity magnetic field and caused a recurrent solar jet event. Second, the evolution of the jets showed blowout-like nature and standard characteristics. Third, the satellite sunspot exhibited a motion toward southeast of AR11166 and merged with the emerging flux near the opposite polarity sunspot penumbra, which afterward, due to flux convergence and cancellation episodes, caused recurrent jets. Fourth, three of the blowout jets associated with coronal mass ejections (CMEs), were observed from field of view of the Solar Terrestrial Relations Observatory. Fifth, almost all the blowout jet eruptions were accompanied with flares or with more intense brightening in the jet base region, while almost standard jets did not manifest such obvious feature during eruptions. The most important, the blowout jets were inclined to faster and larger scale than the standard jets. The standard jets instead were inclined to relative longer-lasting. The obvious shearing and twisting motions of the magnetic field may be interpreted as due to the shearing and twisting motions for a blowout jet eruption. From the statistical results, about 30% blowout jets directly developed into CMEs. It suggests that the blowout jets and CMEs should have a tight relationship.Comment: ApJ 18 pages, 7 figure

Numerical Study on Indoor Wideband Channel Characteristics with Different Internal Wall

Effects of material and configuration of the internal wall on the performance of wideband channel are investigated by using the Finite Difference Time-Domain (FDTD) method. The indoor wideband channel characteristics, such as the path-loss, Root-Mean-Square (RMS) delay spread and number of the multipath components (MPCs), are presented. The simulated results demonstrate that the path-loss and MPCs are affected by the permittivity, dielectric loss tangent and thickness of the internal wall, while the RMS delay spread is almost not relevant with the dielectric permittivity. Furthermore, the comparison of simulated result with the measured one in a simple scenario has validated the simulation study

Spatial imaging of Zn and other elements in Huanglongbing-affected grapefruit by synchrotron-based micro X-ray fluorescence investigation

Huanglongbing (HLB) is a highly destructive, fast-spreading disease of citrus, causing substantial economic losses to the citrus industry worldwide. Nutrient levels and their cellular distribution patterns in stems and leaves of grapefruit were analysed after graft-inoculation with lemon scions containing 'Candidatus Liberibacter asiaticus' (Las), the heat-tolerant Asian type of the HLB bacterium. After 12 months, affected plants showed typical HLB symptoms and significantly reduced Zn concentrations in leaves. Micro-XRF imaging of Zn and other nutrients showed that preferential localization of Zn to phloem tissues was observed in the stems and leaves collected from healthy grapefruit plants, but was absent from HLB-affected samples. Quantitative analysis by using standard references revealed that Zn concentration in the phloem of veins in healthy leaves was more than 10 times higher than that in HLB-affected leaves. No significant variation was observed in the distribution patterns of other elements such as Ca in stems and leaves of grapefruit plants with or without graft-inoculation of infected lemon scions. These results suggest that reduced phloem transport of Zn is an important factor contributing to HLB-induced Zn deficiency in grapefruit. Our report provides the first in situ, cellular level visualization of elemental variations within the tissues of HLB-affected citrus. © 2014 © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology

Exploring data-driven building energy-efficient design of envelopes based on their quantified impacts

Building performance design plays a key role in reducing the energy consumption of buildings. However, the widely used simulation-based design is facing several challenges, such as the labor-intensive modeling process and the performance gaps between design stage estimations and operational energy use. For these reasons, artificial intelligent methods are expected by designers to improve the efficiency and reliability of building energy-efficient design. To date, there has not been a practical data-driven design method of envelopes. This study aimed at exploring data-driven building energy-efficient design of envelopes based on their quantified impacts. A feature selection method and a game-theoretic method were applied to quantify the impacts of envelopes on space heating and cooling energy, which were performed on two building datasets, one of which is from the U.S. and the other from China. Random forest classifiers were developed to conduct the study. Based on discovered energy patterns and quantified impacts of envelopes on energy consumption, a rectified linear design method of envelopes was proposed with the idea of improving the performance of high-impact envelopes. Besides, a validation study was conducted on two office buildings in the hot-summer cold-winter region. To design the envelopes of a building, the data-driven analysis was driven by its similar buildings other than the whole dataset. Moreover, a detailed energy simulation was conducted to evaluate the energy performance of different design solutions. The results showed that compared with baseline design solutions, new strategies could save 1.05%–21.2% energy for space heating and cooling for these two case buildings. The proposed method is a general building envelope design approach and allows designers to easily find an energy-efficient configuration of envelopes. This study demonstrated the feasibility and effectiveness of the data-driven energy-efficient design of building envelopes

Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors

In our paper, we study the vulnerability in cascading failures of the real-world network (power grid) under intentional attacks. Here, we use three indexes (B, K, k-shell) to measure the importance of nodes; that is, we define three attacks, respectively. Under these attacks, we measure the process of cascade effect in network by the number of avalanche nodes, the time steps, and the speed of the cascade propagation. Also, we define the node’s bearing capacity as a tolerant parameter to study the robustness of the network under three attacks. Taking the power grid as an example, we have obtained a good regularity of the collapse of the network when the node’s affordability is low. In terms of time and speed, under the betweenness-based attacks, the network collapses faster, but for the number of avalanche nodes, under the degree-based attack, the number of the failed nodes is highest. When the nodes’ bearing capacity becomes large, the regularity of the network’s performances is not obvious. The findings can be applied to identify the vulnerable nodes in real networks such as wireless sensor networks and improve their robustness against different attacks

Local free-fall temperature of a RN-AdS black hole

We use the global embedding Minkowski space (GEMS) geometries of a (3+1)-dimensional curved Reissner-Nordstr\"om(RN)-AdS black hole spacetime into a (5+2)-dimensional flat spacetime to define a proper local temperature, which remains finite at the event horizon, for freely falling observers outside a static black hole. Our extended results include the known limiting cases of the RN, Schwarzschild--AdS, and Schwarzschild black holes.Comment: 18 pages, 11 figures, version to appear in Int. J. Mod. Phys.

A review of data-driven building performance analysis and design on big on-site building performance data

Building performance design (BPD) is a crucial pathway to achieve high-performance buildings. Previous simulation-based BPD is being questioned due to the performance gaps between simulated and measured values. In recent years, accumulated on-site building performance data (OBPD) make it possible to analyze and design buildings with data-driven methods. This article makes a review of previous studies that conducted data-driven building performance analysis and design on a large amount of OBPD. The covered studies are summarized by the applied techniques, i.e., statistics, regression, classification, and clustering. The data used by these studies are compared and discussed emphasizing the data size and public availability. A comprehensive discussion is given about the achievements of existing studies, and challenges for boosting data-driven BPD from three aspects, i.e., developing data-driven models, the availability of building performance data, and stimulation of industrial practices. The review results indicate that data-driven methods were commonly applied to estimate energy consumptions, and explore energy trends, determinant features, and reference buildings. Identifying determinant features is one of the most successful applications. This study highlights the future research gaps for boosting data-driven building performance design