Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model

Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R2=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R2=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R2=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content

A combined method for gas-bearing layer identification in a complex sandstone reservoir

Langgu Depression is a mature oil and gas exploration area with complicated lithological and physical properties. The varying formation fluid, low-resistivity hydrocarbon-bearing reservoirs, and non-uniform logging series greatly increase the difficulty of gas reservoir identification. The Monte Carlo method is employed to simulate the neutron–gamma logging responses to gas saturation and the influential factors. According to the result, a new gas identification chart eliminating the influence of porosity and formation water salinity is proposed to identify gas reservoirs in the old wells. At the same time, a fluid factor extracted from array acoustic logging and core measurement data is sensitive to the development of gas-bearing layers and useful for the identification of gas reservoirs in the new wells with array acoustic logging. The field examples show that the new combined method greatly improves the ability to identify gas-bearing layers and works well in old well reexamination and new well interpretation

Development Strategy of Nuclear Safety Technology in China

Nuclear safety is a key component of the national security system, and it is the foundation and lifeline of the nuclear industry. Advanced and reliable nuclear safety technology is crucial for maintaining and improving intrinsic safety. Therefore, conducting strategic research on nuclear safety technologies is important for enhancing the nuclear industry in China. In this article, we conduct an in-depth research on China’s nuclear safety technology system using methods including academician interviews, field surveys, conference discussion, and literature review. The results show that, guiding by the overall national security and the nuclear safety concepts, China’s nuclear safety technology has made significant progress in recent years and its nuclear safety performance is good. However, China’s nuclear safety technology system still face several bottleneck problems. For example, the nuclear safety standards system needs improvement, the overall planning of nuclear safety software research and development is insufficient, and the precision and advanced nuclear safety equipment still depends on foreign countries. To continuously modernize the nuclear safety governance system and governance capacities and strengthen China’s nuclear industry, several suggestions are proposed. First, the nuclear safety standards system should be further improved. Second, independent nuclear safety software with high quality should be promoted by coordinating scientific research resources to tackle key problems. Third, government, industry, university, research, and application need to be coordinated to research and develop high-end nuclear safety equipment

Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence

Cotton yield estimation is of great practical significance to producers, allowing them to make rational management decisions. At present, crop yield estimation methods mainly comprise traditional agricultural yield estimation methods, which have many shortcomings. As an ideal “probe” for detecting crop photosynthesis, sun-induced chlorophyll fluorescence (SIF) can directly reflect the dynamics of actual crop photosynthesis and has the potential to predict crop yield, in order to realize cotton yield estimation based on canopy SIF. In this study, we set up field trials with different nitrogen fertilizer gradients. The changes of canopy SIF and the physiological parameters of cotton in different growth periods were analyzed. To investigate the effects of LAI and AGB on canopy SIF estimation of cotton yield, four algorithms, Ada Boost (Adaptive Boosting), Bagging (Bootstrap Aggregating), RF (Random Forest), and BPNN (Backpropagation Neural Network), were used to construct cotton yield estimation models based on the SIF and SIFy (the normalization of SIF by incident photosynthetically active radiation) for different time and growth periods. The results include the following: (1) The effects of the leaf area index (LAI) and aboveground biomass (AGB) on cotton canopy SIF and cotton yield were similar. The correlation coefficients of LAI and AGB with cotton yield and SIF were significantly positively correlated with each other starting from the budding period, reaching the maximum at the flowering and boll period, and decreasing at the boll period; (2) In different monitoring time periods, the R2 of the cotton yield estimation model established based on SIF and SIFy showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00, while the optimal observation time was from 14:00 to 15:00. The R2 increased with the progression of growth from the budding period to the flowering and boll period and decreased at the boll period, while the optimum growth period was the flowering and boll period; (3) Compared to SIF, SIFy has a superior estimation of yield. The best yield estimation model based on the RF algorithm (R2 = 0.9612, RMSE = 66.27 kg·ha−1, RPD = 4.264) was found in the canopy SIFy of the flowering and boll period at 14:00–15:00, followed by the model utilizing the Bagging algorithm (R2 = 0.8898) and Ada Boost algorithm (R2 = 0.8796). In summary, SIFy eliminates the effect of PAR (photosynthetically active radiation) on SIF and can further improve the estimation of SIF production. This study provides empirical support for SIF estimation of cotton yield and methodological and modeling support for the accurate estimation of cotton yield

Inducing high coercivity in MoS2 nanosheets by transition element doping

MoS2 nanosheets were doped with vanadium (V) with a variety of concentrations using a hydrothermal method. Raman, X-ray photoelectron spectroscopy, and electron paramagnetic resonance results indicate the effective substitutional doping in MoS2. Without V doping, oxides such as MoO2 and MoO3 have been observed, whereas with 5 at% V doping, the oxide disappeared. Magnetic measurements show that room temperature ferromagnetism has been induced by V doping. Magnetization tends to increase with the increased V doping concentration. A very large coercivity up to 1.87 kOe has been observed in 5 at% vanadium doped MoS2, which may attribute to a combination effect of localized charge transfer between V and S ions, pinning effect due to the in-between defects, stress induced by doping, and shape anisotropy due to two-dimensional nature of MoS2 ribbons

Intrinsic or interface clustering-induced ferromagnetism in Fe-doped In2O3-diluted magnetic semiconductors

Five percent Fe-doped In2O3 films were deposited using a pulsed laser deposition system. X-ray diffraction and transmission electron microscopy analysis show that the films deposited under oxygen partial pressures of 10–3 and 10–5 Torr are uniform without clusters or secondary phases. However, the film deposited under 10–7 Torr has a Fe-rich phase at the interface. Magnetic measurements demonstrate that the magnetization of the films increases with decreasing oxygen partial pressure. Muon spin relaxation (μSR) analysis indicates that the volume fractions of the ferromagnetic phases in PO2 = 10–3, 10–5, and 10–7 Torr-deposited samples are 23, 49, and 68%, respectively, suggesting that clusters or secondary phases may not be the origin of the ferromagnetism and that the ferromagnetism is not carrier-mediated. We propose that the formation of magnetic bound polarons is the origin of the ferromagnetism. In addition, both μSR and polarized neutron scattering demonstrate that the Fe-rich phase at the interface has a lower magnetization compared to the uniformly distributed phases