Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic.

Monitoring the fine spatiotemporal distribution of urban GDP is a critical research topic for assessing the impact of the COVID-19 outbreak on economic and social growth. Based on nighttime light (NTL) images and urban land use data, this study constructs a GDP machine learning and linear estimation model. Based on the linear model with better effect, the monthly GDP of 34 cities in China is estimated and the GDP spatialization is realized, and finally the GDP spatiotemporal correction is processed. This study analyzes the fine spatiotemporal distribution of GDP, reveals the spatiotemporal change trend of GDP in China's major cities during the current COVID-19 pandemic, and explores the differences in the economic impact of the COVID-19 pandemic on China's major cities. The result shows: (1) There is a significant linear association between the total value of NTL and the GDP of subindustries, with R2 models generated by the total value of NTL and the GDP of secondary and tertiary industries being 0.83 and 0.93. (2) The impact of the COVID-19 pandemic on the GDP of cities with varied degrees of development and industrial structures obviously varies across time and space. The GDP of economically developed cities such as Beijing and Shanghai are more affected by COVID-19, while the GDP of less developed cities such as Xining and Lanzhou are less affected by COVID-19. The GDP of China's major cities fell significantly in February. As the COVID-19 outbreak was gradually brought under control in March, different cities achieved different levels of GDP recovery. This study establishes a fine spatial and temporal distribution estimation model of urban GDP by industry; it accurately monitors and assesses the spatial and temporal distribution characteristics of urban GDP during the COVID-19 pandemic, reveals the impact mechanism of the COVID-19 pandemic on the economic development of major Chinese cities. Moreover, economically developed cities should pay more attention to the spread of the COVID-19 pandemic. It should do well in pandemic prevention and control in airports and stations with large traffic flow. At the same time, after the COVID-19 pandemic is brought under control, they should speed up the resumption of work and production to achieve economic recovery. This study provides scientific references for COVID-19 pandemic prevention and control measures, as well as for the formulation of urban economic development policies.

Estimation and Analysis of PM2.5 Concentrations with NPP-VIIRS Nighttime Light Images: A Case Study in the Chang-Zhu-Tan Urban Agglomeration of China.

Rapid economic and social development has caused serious atmospheric environmental problems. The temporal and spatial distribution characteristics of PM2.5 concentrations have become an important research topic for sustainable social development monitoring. Based on NPP-VIIRS nighttime light images, meteorological data, and SRTM DEM data, this article builds a PM2.5 concentration estimation model for the Chang-Zhu-Tan urban agglomeration. First, the partial least squares method is used to calculate the nighttime light radiance, meteorological elements (temperature, relative humidity, and wind speed), and topographic elements (elevation, slope, and topographic undulation) for correlation analysis. Second, we construct seasonal and annual PM2.5 concentration estimation models, including multiple linear regression, support random forest, vector regression, Gaussian process regression, etc., with different factor sets. Finally, the accuracy of the PM2.5 concentration estimation model that results in the Chang-Zhu-Tan urban agglomeration is analyzed, and the spatial distribution of the PM2.5 concentration is inverted. The results show that the PM2.5 concentration correlation of meteorological elements is the strongest, and the topographic elements are the weakest. In terms of seasonal estimation, the spring estimation results of multiple linear regression and machine learning estimation models are the worst, the winter estimation results of multiple linear regression estimation models are the best, and the annual estimation results of machine learning estimation models are the best. At the same time, the study found that there is a significant difference in the temporal and spatial distribution of PM2.5 concentrations. The methods in this article overcome the high cost and spatial resolution limitations of traditional large-scale PM2.5 concentration monitoring, to a certain extent, and can provide a reference for the study of PM2.5 concentration estimation and prediction based on satellite remote sensing technology.

Nitrogen migration in products during the microwave-assisted hydrothermal carbonization of spirulina platensis.

Nitrogen has a vital influence on the properties of the microwave-assisted hydrothermal carbonization (MHTC) products of Spirulina platensis (SP). The effects of hydrothermal temperature (140-220 °C) and time (1-4 h) on the product distribution and nitrogen migration of SP in MHTC were studied. Increasing temperature led to an increase in the carbon content, and a decrease in the nitrogen content in hydrochar. Protein-N was the major nitrogen-containing species in hydrochar. The total nitrogen in liquid phase increased significantly with increasing temperature. Carbon dots were found to be one of the valuable products in the liquid phase. Higher temperatures improved the amine-N level and reduced the quaternary-N content in carbon dots. A close correspondence was found between the N-containing species and the luminescence centers of carbon dots. A possible nitrogen migration mechanism was proposed to provide guidance for the potential application of the products.

Lightweight Deep Neural Network Method for Water Body Extraction from High-Resolution Remote Sensing Images with Multisensors.

Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction methods, it is difficult to select image texture and features, the shadows of buildings and other ground objects are in the same spectrum as water bodies, the existing deep convolutional neural network is difficult to train, the consumption of computing resources is large, and the methods cannot meet real-time requirements. In this paper, a water body extraction method based on lightweight MobileNetV2 is proposed and applied to multisensor high-resolution remote sensing images, such as GF-2, WorldView-2 and UAV orthoimages. This method was validated in two typical complex geographical scenes: water bodies for farmland irrigation, which have a broken shape and long and narrow area and are surrounded by many buildings in towns and villages; and water bodies in mountainous areas, which have undulating topography, vegetation coverage and mountain shadows all over. The results were compared with those of the support vector machine, random forest and U-Net models and also verified by generalization tests and the influence of spatial resolution changes. First, the results show that the F1-score and Kappa coefficients of the MobileNetV2 model extracting water bodies from three different high-resolution images were 0.75 and 0.72 for GF-2, 0.86 and 0.85 for Worldview-2 and 0.98 and 0.98 for UAV, respectively, which are higher than those of traditional machine learning models and U-Net. Second, the training time, number of parameters and calculation amount of the MobileNetV2 model were much lower than those of the U-Net model, which greatly improves the water body extraction efficiency. Third, in other more complex surface areas, the MobileNetV2 model still maintained relatively high accuracy of water body extraction. Finally, we tested the effects of multisensor models and found that training with lower and higher spatial resolution images combined can be beneficial, but that using just lower resolution imagery is ineffective. This study provides a reference for the efficient automation of water body classification and extraction under complex geographical environment conditions and can be extended to water resource investigation, management and planning.

Employing low-temperature barriers to achieve strain-relaxed and high-performance GaN-based LEDs.

The epitaxial structure design of low-temperature barriers has been adopted to promote strain relaxation in multiple quantum well (MQWs) and achieve high-efficient GaN-based light-emitting diodes (LEDs). With these barriers, the relaxation value of wells increases from 0 to 4.59%. The strain-relaxed mechanism of low-temperature barriers is also discussed. The LED chip with the barriers grown at the TMIn flow of 75 sccm and the growth temperature of 830 °C has an optimal strain relaxation value of 1.53% in wells, and exhibits the largest light output power of 63.83 mW at the injection current of 65 mA, which is higher than that of conventional LED (51.89 mW) by 23%. In-depth studies reveal that the optimal low-temperature barriers remarkably promote the strain relaxation in wells without forming large density of crystalline defects. This achievement of high-efficiency LEDs sheds light on the future solid-state lighting applications.

GaN-based light-emitting diodes on various substrates: a critical review.

GaN and related III-nitrides have attracted considerable attention as promising materials for application in optoelectronic devices, in particular, light-emitting diodes (LEDs). At present, sapphire is still the most popular commercial substrate for epitaxial growth of GaN-based LEDs. However, due to its relatively large lattice mismatch with GaN and low thermal conductivity, sapphire is not the most ideal substrate for GaN-based LEDs. Therefore, in order to obtain high-performance and high-power LEDs with relatively low cost, unconventional substrates, which are of low lattice mismatch with GaN, high thermal conductivity and low cost, have been tried as substitutes for sapphire. As a matter of fact, it is not easy to obtain high-quality III-nitride films on those substrates for various reasons. However, by developing a variety of techniques, distincts progress has been made during the past decade, with high-performance LEDs being successfully achieved on these unconventional substrates. This review focuses on state-of-the-art high-performance GaN-based LED materials and devices on unconventional substrates. The issues involved in the growth of GaN-based LED structures on each type of unconventional substrate are outlined, and the fundamental physics behind these issues is detailed. The corresponding solutions for III-nitride growth, defect control, and chip processing for each type of unconventional substrate are discussed in depth, together with a brief introduction to some newly developed techniques in order to realize LED structures on unconventional substrates. This is very useful for understanding the progress in this field of physics. In this review, we also speculate on the prospects for LEDs on unconventional substrates.

Microstructures and growth mechanisms of GaN films epitaxially grown on AlN/Si hetero-structures by pulsed laser deposition at different temperatures.

2 inch-diameter GaN films with homogeneous thickness distribution have been grown on AlN/Si(111) hetero-structures by pulsed laser deposition (PLD) with laser rastering technique. The surface morphology, crystalline quality, and interfacial property of as-grown GaN films are characterized in detail. By optimizing the laser rastering program, the ~300 nm-thick GaN films grown at 750 °C show a root-mean-square (RMS) thickness inhomogeneity of 3.0%, very smooth surface with a RMS surface roughness of 3.0 nm, full-width at half-maximums (FWHMs) for GaN(0002) and GaN(102) X-ray rocking curves of 0.7° and 0.8°, respectively, and sharp and abrupt AlN/GaN hetero-interfaces. With the increase in the growth temperature from 550 to 850 °C, the surface morphology, crystalline quality, and interfacial property of as-grown ~300 nm-thick GaN films are gradually improved at first and then decreased. Based on the characterizations, the corresponding growth mechanisms of GaN films grown on AlN/Si hetero-structures by PLD with various growth temperatures are hence proposed. This work would be beneficial to understanding the further insight of the GaN films grown on Si(111) substrates by PLD for the application of GaN-based devices.

Highly-efficient GaN-based light-emitting diode wafers on La 0.3 Sr 1.7 AlTaO6 substrates.

Highly-efficient GaN-based light-emitting diode (LED) wafers have been grown on La 0.3 Sr 1.7 AlTaO6 (LSAT) substrates by radio-frequency molecular beam epitaxy (RF-MBE) with optimized growth conditions. The structural properties, surface morphologies, and optoelectronic properties of as-prepared GaN-based LED wafers on LSAT substrates have been characterized in detail. The characterizations have revealed that the full-width at half-maximums (FWHMs) for X-ray rocking curves of GaN(0002) and GaN(10-12) are 190.1 and 210.2 arcsec, respectively, indicating that high crystalline quality GaN films have been obtained. The scanning electron microscopy and atomic force microscopy measurements have shown the very smooth p-GaN surface with the surface root-mean-square (RMS) roughness of 1.3 nm. The measurements of low-temperature and room-temperature photoluminescence help to calculate the internal quantum efficiency of 79.0%. The as-grown GaN-based LED wafers have been made into LED chips with the size of 300 × 300 µm(2) by the standard process. The forward voltage, the light output power and the external quantum efficiency for LED chips are 19.6 W, 2.78 V, and 40.2%, respectively, at a current of 20 mA. These results reveal the high optoelectronic properties of GaN-based LEDs on LSAT substrates. This work brings up a broad future application of GaN-based devices.