Physiologic and metabolic responses of wheat seedlings to elevated and super-elevated carbon dioxide

The metabolic consequence of suboptimal (400 μmol mol⁻¹ or ppm), near-optimal (1500 ppm) and supra-optimal (10,000 ppm) atmospheric carbon dioxide concentrations [CO₂] was investigated in an attempt to reveal plausible underlying mechanisms for the differential physiological and developmental responses to increasing [CO₂]. Both non-targeted and targeted metabolite profiling by GC–MS and LC–MS were employed to examine primary and secondary metabolites in wheat (Triticum aestivum, cv Yocoro rojo) continuously exposed to these [CO₂] levels for 14, 21 and 28 days. Metabolite profile was altered by both [CO₂] and physiological age. In general, plants grown under high [CO₂] exhibited a metabolite profile characteristic of older plants under ambient CO₂. Elevated [CO₂] resulted in higher levels of phosphorylated sugar intermediates, though no clear trend in the content of reducing sugars was observed. Transient starch content was enhanced by increasing [CO₂] to a much greater extent at 10,000 ppm CO₂ than at 1500 ppm CO₂. The percentage increase of starch content resulting from CO₂ enrichment declined as plants develope. In contrast, elevated [CO₂] promoted the accumulation of secondary metabolites (flavonoids) progressively to a greater extent as plants became mature. Elevated [CO₂] to 1500 ppm induced a higher initial growth rate, while super-elevated [CO₂] appeared to negate such initial growth promotion. However, after 4 weeks, there was no difference in vegetative growth between 1500 and 10,000 ppm CO₂-grown plants, both elevated CO₂ levels resulted in an overall 25% increase in biomass over the control plants. More interestingly, elevated atmospheric [CO₂] reduced evapotranspiration rate (ET), but further increase to the supra-optimal level resulted in increased ET (a reversed trend), i.e. ET at 1500 ppm < ET at 10,000 ppm < ET at 400 ppm. The differential effect of elevated and super-elevated CO₂ on plants was further reflected in the nitrogen dynamics. These results provide the potential metabolic basis for the differential productivity and stomatal function of plants grown under elevated and super-elevated CO₂ levels.     

Friction and Wear Properties of Amorphous Carbon Nitride Coatings in Water Lubrication

The friction and wear properties of amorphous carbon nitride(a-CN x)coatings in water lubrication were reviewed.The influences of mating materials and tribological variables such as normal load(W)and sliding speed(V)on the friction and wear properties of the a-CN x coatings were analyzed.It was indicated that the specific wear rate of the a-CN x coatings was related to the hydration reaction of mating materials with water.If the mating materials were easily hydrated,the specific wear rate of a-CN x coatings was low.The water-lubricated properties of the a-CN x coatings were better in comparison to the a-C coatings.The a-CN x/Si-based non-oxide ceramics tribo-pairs exhibited the lowest friction coefficient and wear rate.To describe their friction and wear properties at the normal loads of 3—15Nand the sliding speeds of 0.05—0.5m/s,the wear-mechanism maps for the a-CN x/SiC(Si3N4)tribo-pairs in water were developed.     

基于华氏宏观经济数学模型的高等职业教育动态投入产出模型

伴随着经济的发展和产业结构的变化,高等职业教育越来越受重视,高职教育的投入产出关系也引起了人们的关注。通过分析高职教育的投入产出关系,应用华氏宏观经济数学模型建立了高等职业教育动态投入产出模型。它对分析高职教育各项投入的效率具有实际意义。     

Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory

Unmanned Aerial Vehicle (UAV) swarms have been foreseen to play an important role in military applications in the future, wherein they will be frequently subjected to different disturbances and destructions such as attacks and equipment faults. Therefore, a sophisticated robustness evaluation mechanism is of considerable importance for the reliable functioning of the UAV swarms. However, their complex characteristics and irregular dynamic evolution make them extremely challenging and uncertain to evaluate the robustness of such a system. In this paper, a complex network theory-based robustness evaluation method for a UAV swarming system is proposed. This method takes into account the dynamic evolution of UAV swarms, including dynamic reconfiguration and information correlation. The paper analyzes and models the aforementioned dynamic evolution and establishes a comprehensive robustness metric and two evaluation strategies. The robustness evaluation method and algorithms considering dynamic reconfiguration and information correlation are developed. Finally, the validity of the proposed method is verified by conducting a case study analysis. The results can further provide some guidance and reference for the robust design, mission planning and decision-making of UAV swarms.     

变截面悬臂梁结构动载荷辨识方法

在航空航天领域，作用在结构上动载荷的确定对结构健康监测是非常必要和重要的。为此，本文以类似机翼结构的变截面悬臂梁结构为研究对象，提出了一种基于光纤光栅传感器与卡尔曼滤波器的动载荷识别方法。首先，根据变截面梁单元形式，推导出变截面梁的质量矩阵与刚度矩阵，建立动力学运动方程。然后，以光纤光栅传感器测得的应变信息作为观测信号，通过卡尔曼滤波器生成的增益矩阵、新息序列矩阵以及协方差矩阵，得到灵敏度矩阵和估计力的增益矩阵。在此基础上，利用广义回归模型及其最小二乘算法，估算出动载荷大小、判断出动载荷激励位置。借助数值仿真与实验手段，分别验证了该方法对于单点正弦激励、方波激励、锯齿波激励以及多点同时激励等工况下的动载荷识别效果。结果表明，本文所提算法具有较好的动载荷识别效果和噪声抑制能力，能够为未来风洞试验和真实飞行试验环境中诸如大展弦比机翼表面气动压力等载荷实时辨识、气动外形自适应控制以及结构健康监测提供技术支撑。     

Accuracy analysis of GNSS-IR snow depth inversion algorithms

In recent years, with the continuous development of Global Navigation Satellite System (GNSS), it has been applied not only to navigation and positioning, but also to Earth surface environment monitoring. At present, when performing GNSS-IR (GNSS Interferometric Reflectometry) snow depth inversion, Lomb-Scargle Periodogram (LSP) spectrum analysis is mainly used to calculate the vertical height from the antenna phase center to the reflection surface. However, it has the problem of low identification of power spectrum analysis, which may lead to frequency leakage. Therefore, Fast Fourier Transform (FFT) spectrum analysis and Nonlinear Least Square Fitting (NLSF) are introduced to calculate the vertical height in this paper. The GNSS-IR snow depth inversion experiment is carried out by using the observation data of P351 station in PBO (Plate Boundary Observatory) network of the United States from 2013 to 2016. Three algorithms are used to invert the snow depth and compared with the actual snow depth provided by the station 490 in the SNOTEL network. The observations data of L1 and L2 bands are respectively used to find the optimal combination between different algorithms further to improve the accuracy of GNSS-IR snow depth inversion. For L1 band, different snow depths correspond to different optimal algorithms. When the snow depth is less than 0.8 m, the inversion accuracy of NLSF algorithm is the highest. When the snow depth is greater than 0.8 m, the inversion accuracy of FFT algorithm is higher. Therefore, according to the different snow depth, a combined algorithm of NLSF + FFT is proposed for GNSS-IR snow depth inversion. Compared with the traditional LSP algorithm, the inversion accuracy of the combined algorithm is improved by 10%. For L2 band data, the results show that the accuracy of snow depth inversion of various algorithms do not change with the variations of snow depth. Among the three single algorithms, the inversion accuracy of FFT algorithm is better than that of LSP and NLSF algorithms.     

Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs

The paper proposes a Virtual Target Guidance (VTG)-based distributed Model Predictive Control (MPC) scheme for formation control of multiple Unmanned Aerial Vehicles (UAVs). First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem (FHOCP) can be solved by swarm intelligent optimization algorithm. Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance. Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function. Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.     

Simulator of pyroshock environment and effect rules of its adjustable parameters

During the launching of spacecraft, the on-board devices will undergo a series of pyroshock environments. In order to verify the reliability of these devices under these pyroshock environments, all of them are needed to take the shock test before launching. This paper has carried out an in-depth research on the simulation method of the pyroshock based on the true explosive excitation. In this study, a simulator containing multiple adjustment parameters is presented and the safety is considered by the design of the protective cover. And the working process of this setup is simulated with the explicit dynamic codes LS-DYNA. What’s more, the effects of the adjustment parameters on the three factors of shock Response Spectrum (SRS) of the resonant board are explored carefully. The rules achieved in this paper are verified by a typical example. The results indicate that the improved simulator can avoid the danger of explosive and make full use of the advantage of actual explosive excitation. And the test condition can be quickly realized at the simulator according to the effect rules of the three adjustable parameters.     

An optimal selection of probability distribution functions for unsupervised land cover classification of PALSAR-2 data

Different types of classification techniques are available in the literature for the classification of Synthetic Aperture Radar (SAR) data into various land cover classes. Various SAR images are available for land cover classification such as ALOS PALSAR (PALSAR-1, PALSAR-2), RADARSAT and ENVISAT. In this paper, we have attempted to explore probability distribution function (pdf) based land cover classification using PALSAR-2 data. Over 20 different statistical distribution functions are analyzed for different classes based on statistical parameters. Probability distribution functions are selected based on Chi-squared goodness of fit test for each individual class. A decision tree based classifier is developed for classification based on the selected pdf functions and its statistical parameters. The proposed classification approach has an accuracy of 83.93%.     

LaBr3(Ce)元件破损在线监测关键核素确定及效率校准

常用的元件破损在线监测方法受外在因素影响较大，以γ能谱中关键裂变产物核素比活度进行在线定量分析是一种可靠的元件破损监测方法。针对新型LaBr₃(Ce)元件破损监测仪，设计了元件破损模拟实验，对逸出的裂变产物采用HPGe和LaBr₃(Ce)探测器进行了对比测量。通过不同冷却时间γ能谱的核素分析，确定了¹³⁵Xe，⁸⁸Kr，¹³⁸Xe，⁸⁸Rb，¹³⁸Cs等可作为LaBr₃(Ce)元件破损γ能谱监测的关键核素。同时，制备了覆盖能量范围(250～2 400)keV，含⁶⁰Co，¹³⁷Cs，¹³³Ba；²⁴¹Am，¹⁵²Eu；¹⁰⁹Cd，⁸⁸Y，⁵⁷Co及²⁴Na的4组放射性标准溶液，在建立的效率校准系统上，校准了LaBr₃(Ce)在线监测仪的效率。在反应堆一次异常情况分析中，已校准的LaBr₃(Ce)元件破损监测仪对关键核素的现场分析结果与HPGe的取样分析结果一致，表明效率校准结果准确，校准方法可靠。