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Plastic film mulching and nitrogen (N) fertilization are two important field management practices used to increase crop yields in rain-fed agriculture in arid and semi-arid areas. These two practices, however, can have opposing effects on soil organic carbon (SOC) stock and turnover. To clarify and analyze the combined effects of these practices, we conducted a 7-year continuous maize cultivation experiment with four treatments: (1) no plastic film mulching without N fertilization (Control), (2) plastic film mulching without N fertilization (PFM), (3) N fertilization without plastic film mulching (N), and (4) plastic film mulching with N fertilization (PFM + N). The 13C natural abundance of in the light and heavy fractions of organic carbon (LFOC and HFOC) was used to differentiate “old” (>7 years) and “new” (

Currently, a concern about power resource constraints in the distribution environment is being voiced increasingly, where the increase of power consumption devices overwhelms the terminal load unaffordable and the quality of power consumption cannot be guaranteed. How to acquire the optimal offloading decision of power resources has become a problem that needs to be addressed urgently. To tackle this challenge, a novel reinforcement learning algorithm named Deep Q Network with a partial offloading strategy (DQNP) is proposed to optimize power resource allocation for high computational demands. In the DQNP, a coupled coordination degree model and Lyapunov algorithm are introduced, which trade-offs and decouples the relationships between local-edge and latency–energy consumption. To derive the optimal offloading decision, the resource computation utility function is selected as the objective function. In addition, model pruning is availed to further improve the training time and inference results. Results show that the proposed offloading mechanism can significantly decrease the function value and decline the weighted sum of latency and energy consumption by an average of 3.61%–7.31% relative to other state-of-the-art algorithms. Additionally, the energy loss in the power distribution process is successfully mitigated; furthermore, the effectiveness of the proposed algorithm is also verified.

The immunity potency upon natural infection or vaccination is the main concern for the vaccine strategy of severe acute respiratory syndrome coronavirus 2（SARS COV-2 variant）, especially the recently reported Omicron variant (B.1.1.529). In this study, 200 recipients immunized with three doses of a COVID-19-inactivated vaccine were enrolled, whose serum samples were collected within 2 months after the third immunization. The neutralizing activity of sera against the pseudotyped Omicron variant, prototype, and Delta variant was determined. Our results demonstrated that the positive neutralization activity was 95.5% for the Omicron variant, 99.5% for the prototype, and 98.5% for the Delta variant. The geometric mean titers （GMT） for the Omicron variant was 49 and maintained sustained immune levels for 2 months, which decreased by 4.9-fold and 3.0-fold compared with the prototype (GMT, 239) and Delta variant (GMT, 148), respectively. In summary, our study demonstrated that three doses of a COVID-19-inactivated vaccine effectively yielded potent cross-neutralizing activity against the Omicron variant at 2 months after the third vaccination.

The prediction technology of remaining useful life has received a lot attention to ensure the reliability and stability of complex mechanical equipment. Due to the large-scale, non-linear, and high-dimensional characteristics of monitoring data, machine learning does not need an exact physical model and prior expert knowledge. It has robust data processing ability, which shows a broad prospect in the field of life prediction of complex mechanical and electrical equipment. Therefore, a remaining useful life prediction algorithm based on Random Forest and Bi-directional Long Short-Term Memory (RF-BiLSTM) is proposed. In the RF-BiLSTM algorithm, RF is utilized to extract health indicators that reflect the life of the equipment. On this basis, a BiLSTM neural network is used to predict the residual life of the device. The effectiveness and advanced performance of RF-BiLSTM are verified in commercial modular aviation propulsion system datasets. The experimental results show that the RMSE of the RF-BiLSTM is 0.3892, which is 47.96%, 84.81%, 38.89%, and 86.53% lower than that of LSTM, SVR, XGBoost, and AdaBoost, respectively. It is verified that RF-BiLSTM can effectively improve the prediction accuracy of the remaining useful life of complex mechanical and electrical equipment, and it has certain application value.

An improved optimization algorithm, namely, multi-strategy-sparrow search algorithm (MSSSA), is proposed to solve highly non-linear optimization problems. In MSSSA, a circle map is utilized to improve the quality of the population. Moreover, the adaptive survival escape strategy (ASES) is proposed to enhance the survival ability of sparrows. In the producer stage, the craziness factor integrated with ASES is introduced to enhance the search accuracy and survival ability. In the scout stage, the ASES facilitates sparrows successful escape from danger. Besides, opposition-based learning or Gaussian–Chachy variation helps optimal individuals escape from local solutions. The performance of the MSSSA is investigated on the well-known 23 basic functions and CEC2014 test suite. Furthermore, the MSSSA is applied to optimize the real-life engineering optimization problems. The results show that the algorithm presents excellent feasibility and practicality compared with other state-of-the-art optimization algorithms.

Africa, the second largest continent in the world, has achieved remarkable economic and political results through exploration and positive development. Ecological security comprehensively reflects the health and integrity of an ecosystem, and it is broadly defined as the security state of a complex artificial ecosystem composed of natural, economic and social factors. Ecological security determines the potential for sustainable development in Africa, especially with its rapidly developing economies. However, there is a lack of information on the ecological security state of the continent as a whole. In this study, we constructed an evaluation system based on a pressure-state-response model and evaluated the ecological security state of all 54 African countries. The results showed that, at the national level, the ecological security state of the countries in Africa differed, as did their spatial and temporal variations from 1995 to 2016. In general, African countries showed relatively good ecological security. The years 2007 and 2001 were the worst and best years, respectively, in terms of ecological security during the study period. At the regional level, North Africa and West Africa had the best and worst ecological security, respectively.

High order phenomena in the visible range and with polarization dependence in the ultraviolet (UV) region of the microphotoluminescence (micro-PL) spectrum in whispering-gallery mode (WGM) ZnO microrod cavity have been thoroughly studied at room temperature. WGM ZnO microrod cavity with good crystallinity is produced by the CVD growth method, and the ZnO microrod structures are characterized by structural and optical methods. Through the micro-PL spectrum measurement of the ZnO microrod, it is found that high-order resonance peaks appeared in the visible region. The different polarization conditions can be adjusted by rotating the angles of the polarizer, and it is proved that the micro-PL spectrum has strong polarization-dependent behavior in the UV region. Our results imply broad application potentials in the study of ZnO microrod-based photonic cavity devices.

The electronic structure and spin polarization properties of monolayer GaP3 induced by transition metal (TM) doping were investigated through a first-principles calculation based on density functional theory. The calculation results show that all the doped systems perform spin polarization properties, and the Fe–doped system shows the greatest spin polarization property with the biggest magnetic moment. Based on the analysis from the projected density of states, it was found that the new spin electronic states originated from the p–d orbital couplings between TM atoms and GaP3 lead to spin polarization. The spin polarization results were verified by calculating the spin density distributions and the charge transfer. It is effective to introduce the spin polarization in monolayer GaP3 by doping TM atoms, and our work provides theoretical calculation supports for the applications of triphosphide in spintronics.

The design of rare earth (RE) bearing steels requires a thorough understanding of the formation tendency of RE involved phases in steels, while searching for binary and ternary compounds with a wide variety of composition and permutation need a remarkable amount of experimentation which is nearly infeasible. In the present work, we perform a thorough search for the RE-contained compounds in steels by a data-driven high-throughput computational approach. The search results indicate that RE may react with O and N to form a large amounts of oxide and nitride inclusions, while only Y participate in the formation of sulfide inclusion Y _2 MnS _4 and Y _2 CaS _4 . For the case of ternary compounds in Fe-based solid solution, it is found that RE prefers to form ternary phases with the non-metallic elements, i.e., B, C, O, P and Si, and only Y is found to combine with metal Cr to form YCr _4 Fe _8 . Finally, our screen suggests that RE can participate in the formation of the nano-scale precipitates of κ-carbides, L1 _2 precipitates and B2 precipitates, but MC and M _2 C carbides.