Shanxi Datong University

About: Shanxi Datong University is a education organization based out in Datong, China. It is known for research contribution in the topics: Catalysis & Black hole. The organization has 1467 authors who have published 1697 publications receiving 18701 citations. The organization is also known as: Shānxī Dàtóng Dàxué.

Broadband Metasurfaces with Simultaneous Control of Phase and Amplitude

Abstract: By combining the freedom of both the structural design and the orientation of split ring resonator antennas, we demonstrate terahertz metasurfaces that are capable of controlling both the phase and amplitude profiles over a very broad bandwidth. As an example, we show that the phase-amplitude metasurfaces can be engineered to control the diffraction orders arbitrarily.

A survey of Cyber-Physical Systems

Abstract: Cyber-Physical Systems (CPSs) are characterized by integrating computation and physical processes. The theories and applications of CPSs face the enormous challenges. The aim of this work is to provide a better understanding of this emerging multi-disciplinary methodology. First, the features of CPSs are described, and the research progresses are summarized from different perspectives such as energy control, secure control, transmission and management, control technique, system resource allocation, and model-based software design. Then three classic applications are given to show that the prospects of CPSs are engaging. Finally, the research challenges and some suggestions for future work are in brief outlined.

Canonical Entropy and Phase Transition of Rotating Black Hole

Abstract: Recently, the Hawking radiation of a black hole has been studied using the tunnel effect method. The radiation spectrum of a black hole is derived. By discussing the correction to spectrum of the rotating black hole, we obtain the canonical entropy. The derived canonical entropy is equal to the sum of Bekenstein–Hawking entropy and correction term. The correction term near the critical point is different from the one near others. This difference plays an important role in studying the phase transition of the black hole. The black hole thermal capacity diverges at the critical point. However, the canonical entropy is not a complex number at this point. Thus we think that the phase transition created by this critical point is the second order phase transition. The discussed black hole is a five-dimensional Kerr-AdS black hole. We provide a basis for discussing thermodynamic properties of a higher-dimensional rotating black hole.

Three-Phase Photocatalysis for the Enhanced Selectivity and Activity of CO2 Reduction on a Hydrophobic Surface

Abstract: The photocatalytic CO2 reduction reaction (CRR) represents a promising route for the clean utilization of stranded renewable resources, but poor selectivity resulting from the competing hydrogen evolution reaction (HER) in aqueous solution limits its practical applicability. In the present contribution a photocatalyst with hydrophobic surfaces was fabricated. It facilitates an efficient three‐phase contact of CO2 (gas), H2O (liquid), and catalyst (solid). Thus, concentrated CO2 molecules in the gas phase contact the catalyst surface directly, and can overcome the mass‐transfer limitations of CO2, inhibit the HER because of lowering proton contacts, and overall enhance the CRR. Even when loaded with platinum nanoparticles, one of the most efficient HER promotion cocatalysts, the three‐phase photocatalyst maintains a selectivity of 87.9 %. Overall, three‐phase photocatalysis provides a general and reliable method to enhance the competitiveness of the CRR.

Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

Abstract: Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas. In this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors. These results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.