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Flooding intensity significantly alters the availability of iron (Fe), zinc (Zn), and cadmium (Cd) in paddy soil. However, the influence of arbuscular mycorrhizal fungi (AMF) on the uptake and transfer of Cd and micronutrients (Fe and Zn) under Cd stress in varying flooding conditions is not well understood. A pot experiment was conducted to investigate the micronutrient homeostasis and Cd uptake and transfer in rice cultivated in Cd-contaminated soil with AMF inoculation under continuous and intermittent flooding conditions. Compared to non-inoculation controls, mycorrhizal inoculation decreased Cd concentration in rice plants under continuous and intermittent flooding, and improved grain yield by 39.2 % for early season rice and 21.1 % for late season rice under continuous flooding. Mycorrhizal inoculation balanced the availability of Zn and Fe and decreased the availability of Cd in soil, lowering the ratios of soil-available Cd to both soil-available Zn and soil Fe2+. These changes led to a redistribution of Zn and Fe concentrations in rice, thereby reducing Cd acquisition in a soil-rice system. Structural equation model (SEM) analysis revealed that mycorrhizal inoculation had a strong direct negative effect on the expression of Zn and Fe-related genes OsNRAMP1, OsIRT1, and OsIRT2 in the roots of rice, which in turn directly affected root Cd concentration. Furthermore, mycorrhizal colonization decreased Cd transfer coefficients from leaves to grains under continuous flooding and from nodes and leaves to grains under intermittent flooding. In the nodes, the Fe concentration and the expression of genes OsIRT1 and OsHMA2 were associated with Cd transfer from the nodes to grains. Similarly, in the leaves, the expression of genes OsZIP1 and OsMTP1 corresponded with Cd transfer from leaves to grains. This study provides insights into the role of AMF in affecting micronutrient concentrations and Cd uptake in rice under varying flooding conditions.

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference (EMI) has become a contentious research problem. In certain mission environments, due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations, the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages. Furthermore, the data interaction between unmanned aerial vehicles (UAVs) can only be performed in specific communication-available stages. Traditional cooperative search algorithms cannot handle such situations well. To solve this problem, this study constructed a distributed model predictive control (DMPC) architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor. An attention mechanism ant-colony optimization (AACO) algorithm is proposed for UAV search-control decision planning. The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information, a priori information, and emergent information of the mission to satisfy different search expectations to the maximum extent. Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

Flooding is a critical factor that limits the establishment of a symbiosis between rice and arbuscular mycorrhizal fungi (AMF) in wetland ecosystems. The distribution of carbon resources in roots and the acclimation strategies of rice to flooding stress in the presence of AMF are poorly understood. We conducted a root box experiment, employing nylon sheets or nylon meshes to create separate fungal chambers that either prevented or allowed the roots and any molecules to pass through. We found that the mycorrhizal colonization rate and the expression of genes OsD14L and OsCERK1, which are involved in fungal perception during symbiosis, both increased in mycorrhizal rice roots following intermittent flooding compared to continuous flooding. Furthermore, AMF inoculation affected root morphological traits, facilitating both shallower and deeper soil exploration. Increased submergence intensity led to carbohydrate deprivation in roots, while high mycorrhizal colonization increased soil oxygen consumption and decreased the neutral lipid concentration in roots. However, mycorrhizal inoculation increased the rice photosynthesis rate and facilitated acclimation to submergence by mediating the expression of the genes OsCIPK15 and OsSUB1A to enhance rice shoot elongation and the sugar concentration in roots as a result of reduced competition for carbon between rice and AMF under different flooding conditions.

Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial in achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology is an important tool for determining LNC. However, the intricate coupling between spectral information and nitrogen remains elusive. To address this, this study proposed an estimation method for LNC that integrates hybrid preferred features with deep learning modeling algorithms based on UAV hyperspectral imagery. The proposed approach leverages XGBoost, Pearson correlation coefficient (PCC), and a synergistic combination of both to identify the characteristic variables for LNC estimation. We then construct estimation models of LNC using statistical regression methods (partial least-squares regression (PLSR)) and machine learning algorithms (random forest (RF); deep neural networks (DNN)). The optimal model is utilized to map the spatial distribution of LNC at the field scale. The study was conducted at the National Agricultural Science and Technology Park, Guangzhou, located in Baiyun District of Guangdong, China. The results reveal that the combined PCC-XGBoost algorithm significantly enhances the accuracy of rice nitrogen inversion compared to the standalone screening approach. Notably, the model built with the DNN algorithm exhibits the highest predictive performance and demonstrates great potential in mapping the spatial distribution of LNC. This indicates the potential role of the proposed model in precision fertilization and the enhancement of nitrogen utilization efficiency in rice cultivation. The outcomes of this study offer a valuable reference for enhancing agricultural practices and sustainable crop management.

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line–Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine’s piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data.

【Objective】There is a large population and strong demand for vegetables in Guangdong-Hong KongMacao Greater Bay Area(hereinafter called "the Greater Bay Area"). The evolution of vegetable production pattern and its influencing factors are studied in order to provide references for the spatial layout optimization and high-quality development of vegetable industry in the Greater Bay Area.【Method】The global autocorrelation indexes of vegetable gross outputs of each year were calculated to judge the regional correlation of vegetable production by using the statistical data at counties of the Greater Bay Area from 2010 to 2020 and the administrative map of the same year. The aggregation area of vegetable production and its spatial pattern evolution in counties were analyzed by local autocorrelation index. Spatial regression model was used to analyze the socio-economic factors affecting vegetable production under spatial correlation.【Result】From 2012 to 2020, the global autocorrelation index passes the test and the value is greater than 0, indicating that the vegetable production at county level forms a positive spatial correlation pattern, and the vegetable production has a certain degree of aggregation in space and increases with inter-annual changes. Through local spatial autocorrelation test and analysis, it is found that vegetable production areas such as Longmen county, Boluo county and Huicheng district shows a high-high aggregation pattern, and gradually forms an aggregation pattern in the northeast of the Greater Bay Area over time. The high-low and low-high spatial patterns of vegetable production are gradually affected by the positive correlation of adjacent areas, and finally forms the same pattern with the surrounding areas. Through the analysis of spatial regression model, it is found that the main factors affecting vegetable production are land, economic level, labor force and agricultural technology input in 2012. After 2016, the main influencing factor is land, while the influence of other factors is not obvious or significant, indicating that the expansion of vegetable planting scale is the first factor of yield increase.【Conclusion】There is a positive spatial correlation in vegetable production in Guangdong-Hong Kong-Macao Greater Bay Area, and gradually forms an industrial core area. In the future, it is necessary to increase investment in agricultural science and technology in the core areas of vegetable production such as Boluo, Longmen, Huicheng and Conghua, to further improve the modernization level of production, continuously drive the surrounding areas to improve the comprehensive industrial benefits, and promote the high-quality development of the vegetable industry.

Phenolic acid is a well-known allelochemical, but also a pollutant in soil and water impeding crop production. Biochar is a multifunctional material widely used to mitigate the phenolic acids allelopathic effect. However, phenolic acid absorbed by biochar can still be released. In order to improve the removal efficiency of phenolic acids by biochar, the biochar-dual oxidant (BDO) composite particles were synthesized in this study, and the underlying mechanism of the BDO particles in ameliorating p-coumaric acid (p-CA) oxidative damage to tomato seed germination was revealed. Upon p-CA treatment, the BDO composite particles application increased the radical length, radical surface area, and germination index by 95.0%, 52.8%, and 114.6%, respectively. Compared to using biochar or oxidants alone, the BDO particles addition resulted in a higher removal rate of p-CA and produced more O2•−, HO•, SO4•− and 1O2 radicals via autocatalytic action, suggesting that BDO particles removed phenolic acid by both adsorption and free radical oxidation. The addition of BDO particles maintained the levels of the antioxidant enzyme activity close to the control, and reduced the malondialdehyde and H2O2 by 49.7% and 49.5%, compared to the p-CA treatment. Integrative metabolomic and transcriptomic analyses revealed that 14 key metabolites and 62 genes were involved in phenylalanine and linoleic acid metabolism, which increased dramatically under p-CA stress but down-regulated with the addition of BDO particles. This study proved that the use of BDO composite particles could alleviate the oxidative stress of phenolic acid on tomato seeds. The findings will provide unprecedented insights into the application and mechanism of such composite particles as continuous cropping soil conditioners.

The Antagonistic Weapon-Target Assignment (AGWTA) problem is a crucial decision issue in Command & Control (C2). Since this is a minimax problem, co-evolutionary algorithms can be used to solve it effectively. However, the co-evolutionary algorithm is originally designed for continuous minimax problems which loses its efficiency to discrete contexts. In this paper, a novel elitism co-evolutionary algorithm is proposed to solve the AGWTA. Firstly, an improved AGWTA model for air combat based on the attack and evasion strategies is proposed. Secondly, an elite cooperative genetic algorithm based on the framework of the co-evolutionary algorithm is put forward. In this proposed algorithm, a problem-specific coding method and evolution operator are designed. Meanwhile, an elite individual update mechanism is presented. Finally, based on the analysis of the relationship between the feasible solutions under the air combat environment, an evaluation index is proposed. Experiments show that the proposed algorithm has higher accuracy than traditional co-evolutionary algorithms for solving AGWTA problems.

Two novel magnetic biochar composites (FeC-H and FeC-P) were synthesized using corn stalks and ferrous sulfate through hydrothermal method and traditional pyrolysis, respectively. The samples were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), vibrating sample magnetometer, and particle size analyzer. Batch experiments were conducted to investigate the ability of those samples to absorb aqueous phosphate. FeC-H had lower surface area than FeC-P, but more hydrophilic functional groups were detected on the rough surface of FeC-H. The impregnated iron was present as Fe3O4 in the prepared magnetic biochar composites. The introduced Fe3O4 resulted in high performance of magnetic separation and also played the role as adsorption sites for phosphate. FeC-H and FeC-P demonstrated higher sorption capacity than bare Fe3O4 due to the highly dispersed and smaller crystalline sizes of Fe3O4 particles loaded in corn stalk derived-biochar support. FeC-H had the best performance, with Langmuir adsorption capacity as high as 5.04 mg/g for phosphate. These results indicate that the magnetic biochar composites prepared from corn stalks by hydrothermal method (FeC-H) have potential as a high-efficiency and cost-effective adsorbent for phosphorus removal from wastewater.

Radiation transform models such as PROSAIL are widely used for crop canopy reflectance simulation and biophysical parameter inversion. The PROSAIL model basically assumes that the canopy is turbid homogenous media with a bare soil background. However, the canopy structure changes when crop growth stages develop, which is more or less a departure from this assumption. In addition, a paddy rice field is inundated most of the time with flooded soil background. In this study, field-scale paddy rice leaf area index (LAI), leaf cholorphyll content (LCC), and canopy chlorophyll content (CCC) were retrieved from unmanned-aerial-vehicle-based hyperspectral images by the PROSAIL radiation transform model using a lookup table (LUT) strategy, with a special focus on the effects of growth-stage development and soil-background signature selection. Results show that involving flooded soil reflectance as background reflectance for PROSAIL could improve estimation accuracy. When using a LUT with the flooded soil reflectance signature (LUTflooded) the coefficients of determination (R2) between observed and estimation variables are 0.70, 0.11, and 0.79 for LAI, LCC, and CCC, respectively, for the entire growing season (from tillering to heading growth stages), and the corresponding mean absolute errors (MAEs) are 21.87%, 16.27%, and 12.52%. For LAI and LCC, high model bias mainly occurred in tillering growth stages. There is an obvious overestimation of LAI and underestimation of LCC for in the tillering growth stage. The estimation accuracy of CCC is relatively consistent from tillering to heading growth stages.

Nutrients were extracted from corn stalks, peanut shells, de-oiled peanut meal, chicken manure, and sewage sludge by a subcritical water (SCW) hydrolysis reaction. Compared with the other feedstock, the aqueous phases extracted from de-oiled peanut meal showed the highest water-soluble organic carbon, amino acid, total nitrogen, and phosphorus contents. The effects of solution pH, final hydrothermal temperature, and reaction time on nutrient extraction from de-oiled peanut meal were investigated. The analysis showed that alkaline reagents promoted liquefaction. The highest yield of the total primary nutrients (82.6%) was obtained with extraction reaction at 180 °C for 1.5 h using 0.1 mol/L KOH. The liquid fraction from this reaction was investigated for its potential use as a fertilizer with germination experiments. A higher germination index and root activity were obtained using the liquid extract with the appropriate dilution. These results indicated that subcritical water hydrothermal treatment is a viable way to recover nutrients from biomass wastes. In addition, de-oiled peanut meal is a suitable feedstock for the production of nutrient-rich liquid extract.

The weapon-target assignment (WTA) problem is a key issue in Command & Control (C2). Asset-based multiobjective static WTA (MOSWTA) problem is known as one of the notable issues of WTA. Since this is an NP-complete problem, multiobjective evolutionary algorithms (MOEAs) can be used to solve it effectively. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a practical and promising multiobjective optimization technique. However, MOEA/D is originally designed for continuous multiobjective optimization which loses its efficiency to discrete contexts. In this study, an improved MOEA/D is proposed to solve the asset-based MOSWTA problem. The defining characteristics of this problem are summarized and analyzed. According to these characteristics, an improved MOEA/D framework is introduced. A novel decomposition mechanism is designed. The mating restriction and selection operation are reformulated. Furthermore, a problem-specific population initialization method is presented to improve the efficiency of the proposed algorithm, and a novel nondominated solution-selection method is put forward to handle the constraints of Pareto front. Appropriate extensions of four MOEA variants are developed in comparison with the proposed algorithm on some generated scenarios. Extensive experiments demonstrate that the proposed method is effective and promising.

The sensor-weapon−target assignment (S-WTA) problem is a crucial decision issue in C4ISR. The cooperative engagement capability (CEC) of sensors and weapons depends on the S-WTA schemes, which can greatly affect the operational effectiveness. In this paper, a mathematical model based on the synthetical framework of the S-WTA problem is established, combining the dependent and independent cooperative engagement modes of sensors and weapons. As this problem is a complex combinatorial optimization problem, a novel genetic algorithm is proposed to improve the solution of this formulated S-WTA model. Based on the prior knowledge of this problem, a problem-specific population initialization method and two novel repair operators are introduced. The performances of the proposed algorithm have been validated on the known benchmarks. Extensive experimental studies compared with three state-of-the-art approaches demonstrate that the proposed algorithm can generate better assignment schemes for the most of the benchmarks.

Dempster-Shafer evidence theory (DST) has shown its great advantages to tackle uncertainty in a wide variety of applications. However, how to quantify the information-based uncertainty of basic probability assignment (BPA) with belief entropy in DST framework is still an open issue. The main work of this study is to define a new belief entropy for measuring uncertainty of BPA. The proposed belief entropy has two components. The first component is based on the summation of the probability mass function (PMF) of single events contained in each BPA, which are obtained using plausibility transformation. The second component is the same as the weighted Hartley entropy. The two components could effectively measure the discord uncertainty and non-specificity uncertainty found in DST framework, respectively. The proposed belief entropy is proved to satisfy the majority of the desired properties for an uncertainty measure in DST framework. In addition, when BPA is probability distribution, the proposed method could degrade to Shannon entropy. The feasibility and superiority of the new belief entropy is verified according to the results of numerical experiments.