ALBERT

Description: Currently, due to the recent unprecedented urbanization and industrialization, energy consumption in China is increasing at an enormous speed. However, this process should go hand in hand with sustainable energy development that is based on three interconnected dimensions: (i) energy security, (ii) energy affordability, and (iii) environmental sustainability. It becomes very obvious that an increase in energy efficiency leads to the increase in both energy security and environmental sustainability. Therefore, inadequate energy efficiency causes energy security and environmental sustainability issues, and thus negatively influences economic development of China (or any other country for that matter). This paper explores the intrinsic relationship among urbanization, industrialization, and energy security, as well as the influencing mechanisms of urbanization and industrialization on energy efficiency using a fixed effect model. The paper employs panel data from 30 provinces in mainland China collected in the time range from 2006 to 2015. Our results demonstrate that urbanization and industrialization can significantly improve energy efficiency. Although energy security level decreases considerably with the rise of energy consumption and population growth, the increase in urbanization and industrialization levels can increase energy security through energy efficiency improvements. Moreover, it appears that changes in disposable income and population structure do not alter the effects of industrialization and urbanization on energy security. We conclude that Chinese provinces with high and low urbanization levels should focus on technological innovation and increase industrial development and technological input, respectively. Local governments in China can formulate policies and regulations and promote urbanization according to local economic development and industrial and population structure. The paper also presents theoretical references and decision support that might help in developing local laws and regulations promoting energy efficiency during urbanization and industrialization.

Description: Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.

Description: Previous study has demonstrated that the riboflavin treatment promoted the early ripening of the ‘Kyoho’ grape berry. However, the molecular mechanism causing this was unclear. In order to reveal the regulation mechanism of riboflavin treatment on grape berry development and ripening, the different berry developmental stages of the ‘Kyoho’ berry treated with 0.5 mmol/L of riboflavin was sampled for transcriptome profiling. RNA-seq revealed that 1526 and 430 genes were up-regulated and down-regulated, respectively, for the comparisons of the treatment to the control. TCseq analysis showed that the expression patterns of most of the genes were similar between the treatment and the control, except for some genes that were related to the chlorophyll metabolism, photosynthesis–antenna proteins, and photosynthesis, which were revealed by the enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The differentially expressed genes and weighted gene co-expression network analysis (WGCNA) analysis identified some significantly differentially expressed genes and some hub genes, including up-regulation of the photosynthesis-related ELIP1 and growth and development-related GDSL; and down-regulation of the oxidative stress-related ATHSP22 and berry softening-related XTH32 and GH9B15. The results suggested that the riboflavin treatment resulted in the variations of the expression levels of these genes, and then led to the early ripening of the ‘Kyoho’ berry.

Description: The large-scale Maoping W–Sn deposit in the Gannan metallogenic belt of the eastern Nanling Range, South China, spatially associated with the Maoping granite pluton, hosts total ore reserves of 103,000 t WO3 and 50,000 t Sn. Two different types of mineralization developed in this deposit: Upper quartz vein-type mineralization, mostly within the Cambrian metamorphosed sandstone and slate, and underneath greisen-type mineralization within the Maoping granite. Cassiterites from both types of mineralization coexist with wolframite. Here we report for the first time in situ U–Pb data on cassiterite and zircon of the Maoping deposit obtained by LA-ICP-MS. Cassiterite from quartz vein and greisen yielded weighted average 206Pb/238U ages of 156.8 ± 1.5 Ma and 156.9 ± 1.4 Ma, respectively, which indicates that the two types of mineralization formed roughly at the same time. In addition, the two mineralization ages are consistent with the emplacement age of the Maoping granite (159.0 ± 1.5 Ma) within error, suggesting a close temporal and genetic link between W–Sn mineralization and granitic magmatism. The two types of mineralization formed at the same magmatic-hydrothermal event. Cassiterite from both types of mineralization shows high Fe, Ta, and Zr contents with a low Zr/Hf ratio, suggesting that the ore-forming fluid should be derived from the highly differentiated Maoping granite pluton. Cassiterite in greisen has higher contents of Nb and Ta but a lower concentration of Ti compared with that in quartz vein, indicating that the formation temperature of greisen-type mineralization is little higher than that of quartz-vein-type mineralization.

Description: Brackish water used for irrigation can restrict crop growth and lead to environmental problems. The alternate irrigation with saline water at different growth stages is still not well understood. Therefore, field trials were conducted during 2015–2018 in the NCP to investigate whether alternate irrigation is practicable for winter wheat production. The treatments comprised rain-fed cultivation (NI), fresh and saline water irrigation (FS), saline and fresh water irrigation (SF), saline water irrigation (SS) and fresh water irrigation (FF). The results showed that the grain yield was increased by 20% under SF and FS treatments compared to NI, while a minor decrease of 2% in grain yield was observed compared with FF treatment. The increased soil salinity and risk of long-term salt accumulation in the soil due to alternate irrigation during peak dry periods was insignificant due to leaching of salts from crop root zone during monsoon season. Although Na+ concentration in the leaves increased with saline irrigation, resulting in significantly lower K+:Na+ ratio in the leaves, the Na+ and K+ concentrations in the roots and grains were not affected. In conclusion, the alternate irrigation for winter wheat is a most promising option to harvest more yield and save fresh water resources.

Description: This study provides an empirical analysis of household technical efficiency and its determinant factors (especially conservation payments) in the context of the Grain for Green program. On the basis of a sample of 225 farm households on the Loess Plateau in 2007, we estimate household technical efficiency using the data envelopment analysis method. In addition to a traditional ordinary least square (OLS) analysis, quantile regression (QR) analysis is also deployed to explore the possible heterogeneous effects of conservation payments and other variables on the technical efficiency across the quantiles. The results suggest that when off-farm activities are taken into account, households have considerable potential for improving their technical efficiency; OLS analysis shows that conservation payments decrease household efficiency, and the QR analysis suggests that the negative impact is significant only for higher performance households; The presence of children, access of households to leased land markets, credit markets, and extension services all show heterogeneous impacts on household efficiency. On the basis of the findings of the study, policies suggestions to improve the program’s effectiveness are provided.

Description: Effective prediction of gas concentrations and reasonable development of corresponding safety measures have important guiding significance for improving coal mine safety management. In order to improve the accuracy of gas concentration prediction and enhance the applicability of the model, this paper proposes a long short-term memory (LSTM) cyclic neural network prediction method based on actual coal mine production monitoring data to select gas concentration time series with larger samples and longer time spans, including model structural design, model training, model prediction, and model optimization to implement the prediction algorithm. By using the minimum objective function as the optimization goal, the Adam optimization algorithm is used to continuously update the weight of the neural network, and the network layer and batch size are tuned to select the optimal one. The number of layers and batch size are used as parameters of the coal mine gas concentration prediction model. Finally, the optimized LSTM prediction model is called to predict the gas concentration in the next time period. The experiment proves the following: The LSTM gas concentration prediction model uses large data volume sample prediction, more accurate than the bidirectional recurrent neural network (BidirectionRNN) model and the gated recurrent unit (GRU) model. The average mean square error of the prediction model can be reduced to 0.003 and the predicted mean square error can be reduced to 0.015, which has higher reliability in gas concentration time series prediction. The prediction error range is 0.0005–0.04, which has better robustness in gas concentration time series prediction. When predicting the trend of gas concentration time series, the gas concentration at the time inflection point can be better predicted and the mean square error at the inflection point can be reduced to 0.014, which has higher applicability in gas concentration time series prediction.

Description: The oxygen (O) and neodymium (Nd) isotopic composition of monazite provides an ideal tracer of metamorphism and hydrothermal activity. Calibration of the matrix effect and monitoring of the external precision of monazite O–Nd isotopes with microbeam techniques, such as secondary ion mass spectrometry (SIMS) and laser ablation-multicollector-inductively coupled plasma-mass spectrometry (LA-MC-ICPMS), require well-characterized natural monazite standards for precise microbeam measurements. However, the limited number of standards available is impeding the application of monazite O–Nd isotopes. Here, we report on the RW-1 monazite as a potential new working reference material for microbeam analysis of O–Nd isotopes. Microbeam measurements by electron probe microanalysis (EPMA), SIMS, and LA-MC-ICPMS at 10–24 µm scales have confirmed that it is homogeneous in both elemental and O–Nd isotopic compositions. SIMS measurements yield δ18O values consistent, within errors, with those obtained by laser fluorination techniques. Precise analyses of Nd isotope by thermal ionization mass spectrometry (TIMS) are consistent with mean results of LA-MC-ICPMS analyses. We recommend δ18O = 6.30‰ ± 0.16‰ (2SD) and 143Nd/144Nd = 0.512282 ± 0.000011 (2SD) as being the reference values for the RW-1 monazite.

Description: Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. A coverless steganographic approach without any modification for transmitting secret color image is proposed. We propose a diversity image style transfer network using multilevel noise encoding. The network consists of a generator and a loss network. A multilevel noise to encode matching the subsequent convolutional neural network scale is used in the generator. The diversity loss is increased in the loss network so that the network can generate diverse image style transfer results. Residual learning is introduced so that the training speed of network is significantly improved. Experiments show that the network can generate stable results with uniform texture distribution in a short period of time. These image style transfer results can be integrated into our coverless steganography scheme. The performance of our steganography scheme is good in steganographic capacity, anti-steganalysis, security, and robustness.

Description: In this work, a carbon black (VXC-72R)/zirconia (ZrO2) nanocomposite-modified glassy carbon electrode (GCE) was designed, and a VXC-72R/ZrO2/GCE-based electrochemical sensor was successfully fabricated for the high-sensitivity detection of methyl parathion (MP). Electrochemical measurements showed that the VXC-72R/ZrO2/GCE-based electrochemical sensor could make full use of the respective advantages of the VXC-72R and ZrO2 nanoparticles to enhance the MP determination performance. The VXC-72R nanoparticles had high electrical conductivity and a large surface area, and the ZrO2 nanoparticles possessed a strong affinity to phosphorus groups, which could achieve good organophosphorus adsorption. On the basis of the synergistic effect generated from the interaction between the VXC-72R and ZrO2 nanoparticles, the VXC-72R/ZrO2/GCE-based electrochemical sensor could show excellent trace analysis determination performance. The low detection limit could reach up to 0.053 μM, and there was a linear concentration range of 1 μM to 100 μM. Such a high performance indicates that the VXC-72R/ZrO2/GCE-based electrochemical sensor has potential in numerous foreground applications.