Motivated by the holographic principle, it has been suggested that the dark energy density may be inversely proportional to the area of the event horizon of the Universe. However, such a model would have a causality problem. In this paper, we propose to replace the future event horizon area with the inverse of the Ricci scalar curvature. We show that this model does not only avoid the causality problem and is phenomenologically viable, but also naturally solves the coincidence problem of dark energy. Our analysis of the evolution of density perturbations show that the matter power spectra and CMB temperature anisotropy is only slightly affected by such modification.

According to Babichev et al., the accretion of a phantom test fluid onto a Schwarzschild black hole will induce the mass of the black hole to decrease, however the backreaction was ignored in their calculation. Using new exact solutions describing black holes in a background Friedmann-RobertsonWalker universe, we find that the physical black hole mass may instead increase due to the accretion of phantom energy. If this is the case, and the future universe is dominated by phantom dark energy, the black hole apparent horizon and the cosmic apparent horizon will eventually coincide and, after that, the black hole singularity will become naked in finite comoving time before the Big Rip occurs, violating the Cosmic Censorship Conjecture.

Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.

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Abstract:No single sensor can acquire complete information by applying one or several multi-surveys to cultural object reconstruction. For instance, a terrestrial laser scanner (TLS) usually obtains information on building facades, whereas aerial photogrammetry is capable of providing the perspective for building roofs. In this study, a camera-equipped unmanned aerial vehicle system (UAV) and a TLS were used in an integrated design to capture 3D point clouds and thus facilitate the acquisition of whole information on an object of interest for cultural heritage. A camera network is proposed to modify the image-based 3D reconstruction or structure from motion (SfM) method by taking full advantage of the flight control data acquired by the UAV platform. The camera network improves SfM performances in terms of image matching efficiency and the reduction of mismatches. Thus, this camera network modified SfM is employed to process the OPEN ACCESS Remote Sens. 2014, 6 10414 overlapping UAV image sets and to recover the scene geometry. The SfM output covers most information on building roofs, but has sparse resolution. The dense multi-view 3D reconstruction algorithm is then applied to improve in-depth detail. The two groups of point clouds from image reconstruction and TLS scanning are registered from coarse to fine with the use of an iterative method. This methodology has been tested on one historical monument in Fujian Province, China. Results show a final point cloud with complete coverage and in-depth details. Moreover, findings demonstrate that these two platforms, which integrate the scanning principle and image reconstruction methods, can supplement each other in terms of coverage, sensing resolution, and model accuracy to create high-quality 3D recordings and presentations.

No abstract

Within a large class of exact solutions of the Einstein equations describing a black hole embedded in a Friedmann universe it is shown that, under certain assumptions, only those with comoving Hawking-Hayward quasi-local mass are generic, in the sense that they are late-time attractors.Comment: 9 pages, LaTex, to appear in Phys. Lett.

Biochar has been widely proposed as a relatively novel approach to improve soil quality and increase crop productivity, but its underlying mechanisms are not well understood. A large root system in plants is either a constitutive or an inducible trait dependent on the uptake of resources and the production of shoot dry matter. Here a field experiment was conducted to investigate the effects of biochar amendment on the dynamic growth and development of maize (Zea mays L.), both above- and belowground, and to explore the relationship between soil condition, root traits and shoot biomass over two growing seasons on the Loess Plateau in northern China. Biochar was added to a maize field at rates of 0, 10, 20 and 30 t ha–1 without mulching and at rates of 0 and 20 t ha–1 with film mulching before sowing the first crop. The application of straw biochar with 30 t ha–1 decreased soil bulk density by 12% and increased soil total porosity by 13% in the 0–10-cm soil layer 6 months after biochar addition. Biochar amendment increased soil organic carbon, total soil nitrogen, carbon : nitrogen ratio, and available phosphorus and potassium at the end of each growing season. Although, root growth was inhibited at a rate of 30 t ha–1 in the early stage of the first year, biochar amendment exhibited a positive effect in other stages, resulting in higher root weight density, root length density and root surface-area density. These responses led to higher growth rates, maize biomass, grain yields and uptake of nitrogen, phosphorus and potassium as the rate of biochar addition increased. Film mulching with biochar amendment achieved the greatest root and shoot biomass and grain yield in both crops, despite differences in climate conditions. Biochar aged in the field for 2 years had the same effect on soil properties and crop production, suggesting that the application of straw biochar may be a promising option for increasing productivity in semi-arid farmland.