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Tree whitewash has the functions of parasite prevention and cold protection and is therefore commonly used in the maintenance and management of trees. At present, tree whitewash mainly relies on manual operation, which has the problems of low efficiency, poor quality, and uneven distribution of the whitewash agent. To address this issue, this study developed a smart tree whitewash device based on human–computer interaction. The device was controlled mainly by a programmable logic controller (PLC). Once the trunk information collected by sensors was received by the PLC, it would control the up and down motions of the ball screw to manipulate the mechanical arm for whitewash. In addition, a Mitsubishi GT12 touch screen was adopted to facilitate system operation. Subsequently, a whitewash experiment was performed on poplar trunks with lengths of 10–35 cm using three different whitewash devices, i.e., a backpack sprayer, a semi-automatic tree sprayer, and the proposed smart tree whitewash device; the efficiency and the amount of whitewash agents used were compared. The results suggested that as the tree diameter at breast height increased, the amount of required whitewash agent elevated accordingly. In this case, the time required by the backpack sprayer and the semiautomatic tree sprayer to complete the job both increased, whereas that required by the smart tree whitewash device remained almost identical. In terms of work efficiency, the time required by the smart whitewash device to whitewash a tree was 109.89 s, which was approximately 1/2 of the time required by the backpack sprayer or 2/3 of that required by the semiautomatic tree spraying device. Meanwhile, the amount of whitewash agent required by the smart whitewash device to whitewash a tree was 140.23 g, which was approximately 0.46 of the amount required by the backpack sprayer or 0.74 of that required by the semiautomatic tree spraying device. Therefore, it was concluded that the proposed smart tree whitewash device could not only improve the work efficiency of tree whitewash but also greatly reduce the amount of whitewash agent required, thereby decreasing the cost and minimising environmental pollution. This study provides theoretical guidance and technical support for future research on smart tree whitewash devices.

The unmanned aerial vehicle (UAV) base station plays a significant role in enhancing the terrestrial network, when the ground base station (GBS) is destroyed in emergent cases or its load exceeds the capacity of the terrestrial network. Presently, many papers focus on optimizing the UAV position deployment and user access, while ignoring the optimization about the spectrum resource management. To solve this problem, we formulate a joint optimization problem of the spectrum resource management and the position placement for UAVs with the constraint of the limited backhaul capacity. Later, the joint optimization problem is modeled as a hierarchical game decision architecture comprised of a UAV position placement game and a spectrum resource management game. Further, we analyze the equilibrium property of the two games and propose two best response- (BR) based optimization algorithms to reach the Nash Equilibriums (NEs) of the two games, respectively. Specifically, the proposed algorithm about the UAV deployment considers the variable granularity local exploration and global random exploration. Simulation results show that the proposed UAV deployment algorithm can improve the total throughput by 7% and 20% at least in comparison with the K-means deployment algorithm and the fixed granularity exploration algorithm, respectively.

Surface ozone concentrations typically peak during the daytime, driven by active photochemical production, and decrease gradually after sunset, due to chemical destruction and dry deposition. Here, we report that nocturnal ozone enhancement (NOE, defined as an ozone increase of more than 5 ppbv h−1 in 1 of any 2 adjacent hours between 20:00 and 06:00 LT, local time) events are observed at multiple monitoring sites in China at a high frequency, which has not been recognized in previous studies. We present an overview of the general characteristics of NOE events in China and explore the possible mechanisms based on 6 years of observations from the national monitoring network. We find that the mean annual frequency of NOE events is 41±10 % (i.e., about 140 d would experience an NOE event per year) averaged over all 814 Chinese sites between 2014 and 2019, which is 46 % larger than that over Europe or the United States. The NOE event frequency is higher in industrialized city clusters (>50 %) than in regions with lighter ozone pollution, and it is higher in the warm season (46 %) than in the cold season (36 %), consistent with the spatiotemporal evolution of ozone levels. The mean ozone peak during NOE events reaches 37±6 ppbv in the warm season. The ozone enhancements are within 5–15 ppbv h−1 during 85 % of the NOE events; however, in about 10 % of cases, the ozone increases can exceed 20 ppbv h−1. We propose that high photochemistry-induced ozone during the daytime provides a rich ozone source in the nighttime residual layer, determining the overall high frequency of NOE events in China, and that enhanced atmospheric mixing then triggers NOE events by allowing the ozone-rich air in the residual layer to mix into the nighttime boundary layer. This is supported by our analyses which show that 70 % (65 %) of the NOE events are associated with increases in friction velocity (planetary boundary layer height), indicative of enhanced atmospheric mixing, and also supported by the observed sharp decreases in surface NO2 and CO concentrations with ozone increases in NOE events, a typical signal of mixing with air in the residual layer. Three case studies in Beijing and Guangzhou show that synoptic processes such as convective storms and low-level jets can lead to NOE events by aggravating vertical mixing. Horizontal transport of ozone-rich plumes may also be a supplementary driver of NOE events. Our results summarize, for the first time, the characteristics and mechanism of NOE events in China based on nationwide and long-term observations, and our findings emphasize the need for more direct measurements and modeling studies on the nighttime ozone evolution from the surface to the residual layer.

Here we present a field measurement of ClNO2 (nitryl chloride) and N2O5 (dinitrogen pentoxide) by a time-of-flight chemical ionization mass spectrometer (ToF-CIMS) with the Filter Inlet for Gas and AEROsols (FIGAERO) at a regional site in the Pearl River Delta during a photochemical pollution season from 26 September to 17 November 2019. Three patterns of air masses are sampled during this campaign, including the dominating air masses from the north and northeast urban regions (Type A), the southeast coast (Type B), and the South China Sea (Type C). The concentration of ClNO2 and N2O5 was observed to be much higher in Type A and B than in Type C, indicating that the urban nighttime chemistry is more active than the background marine regions. The N2O5 uptake coefficient and ClNO2 production yield were estimated based on the field measurement, and the performance of the previously derived parameterizations was assessed. The nighttime ClNO2 correlated with particulate chloride and the mass concentration of fine particles (most likely due to aerosol surface area) suggested that the ClNO2 formation was limited by the N2O5 uptake at this site. By examining the relationship between particulate chloride and other species, we implied that anthropogenic emissions (e.g., biomass burning) rather than sea salt particles dominate the origin of particulate chloride, although the site was only about 100 km away from the ocean. A box model with detailed chlorine chemistry is used to investigate the impacts of ClNO2 chemistry on atmospheric oxidation. Model simulations showed that the chlorine radical liberated by ClNO2 photolysis during the next day had a slight increase in concentrations of OH, HO2, and RO2 radicals, as well as minor contributions to RO2 radical and O3 formation (< 5 %, on daytime average), in all the three types of air masses. Relatively high contributions were observed in Type A and B. The overall low contributions of ClNO2 to atmospheric oxidation are consistent with those reported recently from wintertime observations in China (including Shanghai, Beijing, Wangdu, and Mt. Tai). This may be attributed to the following: (1) relatively low particle mass concentration limited ClNO2 formation; (2) other reactions channels, like nitrous acid (HONO), oxygenated volatile organic compounds (OVOCs, including formaldehyde), and ozone photolysis had a more significant radical formation rate during the ozone pollution episodes and weakened the ClNO2 contribution indirectly. The results provided scientific insights into the role of nighttime chemistry in photochemical pollution under various scenarios in coastal areas.

High concentrations of nitrous acid (HONO) have been observed in the Pearl River Delta (PRD) region of China in recent years, contributing to an elevated atmospheric oxidation capacity due to the production of OH through HONO photolysis. We investigated the budget of HONO at an urban site in Guangzhou from 27 September to 9 November 2018 using data from a comprehensive atmospheric observation campaign. During this period, measured concentrations of HONO were 0.02 to 4.43 ppbv, with an average of 0.74 ± 0.70 ppbv. An emission ratio (HONO/NOx) of 0.9 ± 0.4 % was derived from 11 fresh plumes. The primary emission rate of HONO at night was calculated to be between 0.04 ± 0.02 and 0.30 ± 0.15 ppbv h−1 based on a high-resolution NOx emission inventory. Heterogeneous conversion of NO2 on the ground surface (0.27 ± 0.13 ppbv h−1), primary emissions from vehicle exhaust (between 0.04 ± 0.02 and 0.30 ± 0.15 ppbv h−1, with a middle value of 0.16 ± 0.07 ppbv h−1), and the homogeneous reaction of NO + OH (0.14 ± 0.30 ppbv h−1) were found to be the three largest sources of HONO at night. Heterogeneous NO2 conversion on aerosol surfaces (0.03 ± 0.02 ppbv h−1) and soil emission (0.019 ± 0.009 ppbv h−1) were two other minor sources. Correlation analysis shows that NH3 and the relative humidity (RH) may have participated in the heterogeneous transformation of NO2 to HONO at night. Dry deposition (0.41 ± 0.31 ppbv h−1) was the main removal process of HONO at night, followed by dilution (0.18 ± 0.16 ppbv h−1), while HONO loss at aerosol surfaces was much slower (0.008 ± 0.006 ppbv h−1). In the daytime, the average primary emission Pemis was 0.12 ± 0.02 ppbv h−1, and the homogeneous reaction POH+NO was 0.79 ± 0.61 ppbv h−1, larger than the unknown source PUnknown (0.65 ± 0.46 ppbv h−1). Similar to previous studies, PUnknown appeared to be related to the photo-enhanced conversion of NO2. Our results show that primary emissions and the reaction of NO + OH can significantly affect HONO at a site with intensive emissions during both the daytime and nighttime. Uncertainty in parameter values assumed in the calculation of HONO sources can have a strong impact on the relative importance of HONO sources at night, and could be reduced by improving knowledge of key parameters such as the NO2 uptake coefficient. The uncertainty in the estimated direct emission can be reduced by using emission data with higher resolution and quality. Our study highlights the importance of better constraining both conventional and novel HONO sources by reducing uncertainties in their key parameters for advancing our knowledge of this important source of atmospheric OH.

The pathogenesis of microbial infections and sepsis is partly attributable to dysregulated innate immune responses propagated by late-acting proinflammatory mediators such as procathepsin L (pCTS-L). It was previously not known whether any natural product could inhibit pCTS-L-mediated inflammation or could be strategically developed into a potential sepsis therapy. Here, we report that systemic screening of a NatProduct Collection of 800 natural products led to the identification of a lipophilic sterol, lanosterol (LAN), as a selective inhibitor of pCTS-L-induced production of cytokines [e.g., Tumor Necrosis Factor (TNF) and Interleukin-6 (IL-6)] and chemokines [e.g., Monocyte Chemoattractant Protein-1 (MCP-1) and Epithelial Neutrophil-Activating Peptide (ENA-78)] in innate immune cells. To improve its bioavailability, we generated LAN-carrying liposome nanoparticles and found that these LAN-containing liposomes (LAN-L) similarly inhibited pCTS-L-induced production of several chemokines [e.g., MCP-1, Regulated upon Activation, Normal T Cell Expressed and Presumably Secreted (RANTES) and Macrophage Inflammatory Protein-2 (MIP-2)] in human blood mononuclear cells (PBMCs). In vivo, these LAN-carrying liposomes effectively rescued mice from lethal sepsis even when the first dose was given at 24 h post the onset of this disease. This protection was associated with a significant attenuation of sepsis-induced tissue injury and systemic accumulation of serval surrogate biomarkers [e.g., IL-6, Keratinocyte-derived Chemokine (KC), and Soluble Tumor Necrosis Factor Receptor I (sTNFRI)]. These findings support an exciting possibility to develop liposome nanoparticles carrying anti-inflammatory sterols as potential therapies for human sepsis and other inflammatory diseases.

Nitrate radical (NO3) is an important nocturnal atmospheric oxidant in the troposphere, which significantly affects the lifetime of pollutants emitted by anthropogenic and biological activities, especially volatile organic compounds (VOC). Here, we used one-year VOC observation data obtained in urban Beijing in 2019 to look insight to the level, compositions and seasonal variation of NO3 reactivity (kNO3). We show the hourly kNO3 towards measured VOC highly varied from -4 to 0.083 s-1 with campaign-averaged value (± standard deviation) of 0.0032 ± 0.0042 s-1. There was large seasonal difference in NO3 reactivity towards VOC with the average of 0.0024 ± 0.0026 s-1, 0.0067 ± 0.0066 s-1, 0.0042 ± 0.0037 s-1, 0.0027 ± 0.0028 s-1 from spring to winter. Alkenes such as isoprene and styrene accounted for the majority. Isoprene was the dominant species in spring, summer, and autumn, accounting for 40.0 %, 77.2 % and 43.2 %, respectively. Styrene only played a leading role in winter with the percentage of 39.8 %. Sensitivity study shows monoterpenes, the species we did not measure, may account a large fraction of kNO3. Based on the correlation between the calculated kNO3 and VOC concentrations in 2019, we established localized parameterization schemes for predicting the reactivity by only using a part of VOC species. The historical published VOC data was collected to reconstruct the long-term NO3 reactivity in Beijing by the parameterization method. The downward trend of kNO3 during 2011–2020 may be responded to the reduction of anthropogenic VOC emission. At last, we revealed that NO3 dominated the nocturnal VOC oxidation with 83 % on the annual average in Beijing in 2019, which varied seasonally and was strongly regulated by the level of kNO3, nitrogen oxide and ozone. Our results improve the understanding of nocturnal atmospheric oxidation in urban regions, and gain the knowledge of nocturnal VOC oxidation and secondary organic pollution.

Accurate positioning and state estimation of surface vessels are prerequisites to achieving autonomous navigation. Recently, the rapid development of 3D LiDARs has promoted the autonomy of both land and aerial vehicles, which has aroused the interest of researchers in the maritime community accordingly. In this paper, the state estimation schemes based on 3D LiDAR scan matching are explored in depth. Firstly, the iterative closest point (ICP) and normal distribution transformation (NDT) algorithms and their variants are introduced in detail. Besides, ten representative registration algorithms are selected from the variants for comparative analysis. Two types of experiments are designed by utilizing the field test data of an ASV equipped with a 3D LiDAR. Both the accuracy and real-time performance of the selected algorithms are systemically analyzed based on the experimental results. It follows that ICP and Levenberg–Marquardt iterative closest point (LMICP) methods perform well on single-frame experiments, while the voxelized generalized iterative closest point (FastVGICP) and multi-threaded optimization generalized iterative closest point (FastGICP) methods have the best performance on continuous-frame experiments. However, all methods have lower accuracy during fast turning. Consequently, the limitations of current methods are discussed in detail, which provides insights for future exploration of accurate state estimation based on 3D LiDAR for ASVs.

This paper studies the user selection problem for a cooperative nonorthogonal multiple access (NOMA) system consisting of a base station, a far user, and N near users. The selected near user receives its own message and assists the far user by relaying the far user’s message. Firstly, we propose a user selection strategy to maximize the selected near user’s data rate while satisfying the quality-of-service (QoS) requirement of the far user. Considering that the channel state information (CSI) of users in actual communication is usually imperfect, we then analyze the outage probability of the NOMA system based on the user selection strategy under imperfect CSI and obtain a closed-form expression. The theoretical analysis shows that the diversity order of the NOMA system under imperfect CSI is 0, which means the multiuser diversity order disappears. In order to improve the impact of imperfect CSI on system performance, we use the deep learning method to identify and classify channels of imperfect CSI and improve the accuracy of CSI. The simulation results show that the theoretical analysis of outage performance is consistent with the numerical results. Compared with the strategy without the deep learning method, the proposed deep learning-based user selection scheme significantly improves the system performance. Furthermore, we verify that our scheme recovers the diversity gain.

Ambient nitrate has been of increasing concern in PM2.5, while there are still large uncertainties in quantifying the formation of nitrate aerosol. The formation pathways of nitrate aerosol at an urban site and a suburban site in the Pearl River Delta (PRD) are investigated using an observation-constrained box model. Throughout the campaigns, aerosol pollution episodes were constantly accompanied with the increase in nitrate concentrations and fractions at both urban and suburban sites. The simulations demonstrate that chemical reactions in the daytime and at night both contributed significantly to formation of nitrate in the boundary layer at the two sites. However, nighttime reactions predominantly occurred aloft in the residual layer at the urban site, and downward transport from the residual layer in the morning is an important source (53 %) for surface nitrate at the urban site, whereas similar amounts of nitrate were produced in the nocturnal boundary layer and residual layer at the suburban site, which results in little downward transport of nitrate from the residual layer to the ground at the suburban site. We show that nitrate formation was in the volatile-organic-compound-limited (VOC-limited) regime at the urban site, and in the transition regime at the suburban site, identical to the response of ozone at both sites. The reduction of VOC emissions can be an efficient approach to mitigate nitrate in both urban and suburban areas through influencing hydroxyl radical (OH) and N2O5 production, which will also be beneficial for the synergistic control of regional ozone pollution. The results highlight that the relative importance of nitrate formation pathways and ozone can be site-specific, and the quantitative understanding of various pathways of nitrate formation will provide insights for developing nitrate and ozone mitigation strategies.

Particulate nitrate (NO3-) is one of the dominant components of fine particles in China, especially during pollution episodes, and has a significant impact on human health, air quality, and climate. Here a comprehensive field campaign that focuses on the atmospheric oxidation capacity and aerosol formation and their effects in the Yangtze River Delta (YRD) was conducted from May to June 2019 at a regional site in Changzhou, Jiangsu Province in China. The concentrations of NO3-, OH radicals, N2O5, NO2, O3, and relevant parameters were measured simultaneously. We showed a high NO3- mass concentration with 10.6 ± 8.9 µg m−3 on average, which accounted for 38.3 % of total water-soluble particulate components and 32.0 % of total PM2.5, followed by the proportion of sulfate, ammonium, and chloride by 26.0 %, 18.0 %, and 2.0 %, respectively. This result confirmed that the heavy nitrate pollution in eastern China happened not only in winter, but also in summer. This study's high nitrate oxidation ratio (NOR) emphasized the solid atmospheric oxidation and fast nitrate formation capacity in the YRD. It was found that OH + NO2 during daytime dominated nitrate formation on clean days, while N2O5 hydrolysis vastly enhanced and became comparable with that of OH + NO2 during polluted days (67.2 % and 30.2 %, respectively). The updated observed-constraint Empirical Kinetic Modeling Approach (EKMA) was used to assess the kinetic controlling factors of both local O3 and NO3- productions, which indicated that the O3-targeted scheme (VOCs : NOx = 2:1) is adequate for mitigating the O3 and nitrate pollution coordinately during summertime in this region. Our results promote the understanding of nitrate pollution mechanisms and mitigation based on field observation and model simulation and call for more attention to nitrate pollution in the summertime.

Traditional methods of cow estrus monitoring technology are not suitable for the current needs of large-scale, intensive and welfare-based farming. There is a need to improve the detection rate of cow estrus and to reduce the emergency response caused by wearing contact devices. Furthermore, it is necessary to verify the practical effectiveness of the LOGISITC and SV (support vector machine) models for modeling cow estrus. In this paper, we have proposed a non-contact cow estrus monitoring method based on the thermal infrared images of cows and have proposed a lab-color-space-based feature extraction method for the thermal infrared images of cow eyes and vulvas. The test subjects were 10 Holstein cows, monitored on a fixed basis, to determine the best segmentation contour. The LOGISTIC and SVM (support vector machine) models were used to establish the cow estrus model using the thermal infrared temperature variation in cows in estrus and cows not in estrus. The experimental results showed that the heat detection rate of the LOGISTIC-based model was 82.37% and the heat detection rate of the SVM-based model was 81.42% under the optimal segmentation profile. The highest temperature in the eye and vulva of cows was the input, and the recall rate was above 86%. The heat monitoring method based on thermal infrared images does not cause stress to cows and meets the needs of modern dairy farming for welfare breeding.

Antibody-based strategies have been attempted to antagonize early cytokines of sepsis, but not yet been tried to target inducible late-acting mediators. Here, we report that the expression and secretion of procathepsin-L (pCTS-L) was induced by serum amyloid A (SAA) in innate immune cells, contributing to its late and systemic accumulation in experimental and clinical sepsis. Recombinant pCTS-L induced interleukin-6 (IL-6), IL-8, GRO-α/KC, GRO-β/MIP-2, and MCP-1 release in innate immune cells and moderately correlated with blood concentrations of these cytokines/chemokines in clinical sepsis. Mechanistically, pCTS-L interacted with Toll-like receptor 4 (TLR4) and the receptor for advanced glycation end products (RAGE) to induce cytokines/chemokines. Pharmacological suppression of pCTS-L with neutralizing polyclonal and monoclonal antibodies attenuated pCTS-L–mediated inflammation by impairing its interaction with TLR4 and RAGE receptors, and consequently rescued animals from lethal sepsis. Our findings have suggested a possibility of developing antibody strategies to prevent dysregulated immune responses mediated by late-acting cytokines.

The goal of this study was to investigate the soil particle-size distribution (PSD) and pore structure characteristics in the Kubuqi Desert in order to provide basic data for gaining insights into the soil-modifying properties of the local vegetation. Based on laser diffraction analysis, we measured the soil PSD and calculated the single and multi-fractal dimensions of the soils under typical artificial forest and natural grassland vegetation. The diameters, volumes and surface areas of the soil pores were determined via nitrogen adsorption experiments. The relationships between the fractal characteristics and pore structure parameters of the soil were investigated via redundancy analysis and stepwise regression. The clay content was less than 2.0%, and the sand content was greater than 75.0%. There was variability in the PSD and fractal dimension, which was mainly observed for the 0–20 cm, 20–80 cm, and 80–100 cm soil layers. In the 0–80 cm, the fine particle content, single fractal dimension (D), entropy dimension (D1), ratio of D1 to the capacity dimension (D1/D0), and correlation dimension (D2) were smaller for the forest soils than for the grassland soils. The fine particle (clay and silt) content of the 80–100 cm soil layer was approximately 37.8% higher for the Salix psammophila-8a than for the Salix psammophila-3a, and it was approximately 161.4% higher for the Populus popular-35a than for the Pinus sylvestris var. mongolica-8a. The silt content, D1, D1/D0, and D2 were significantly positively correlated with the specific surface area (SSA), total pore volume (TPV), and average pore diameter (APD) of the nanopores (p < 0.05, 0.01, or 0.001), and they were significantly negatively correlated with the percentage of the micropore volume (PMV) (p < 0.05 or 0.01). In the Kubuqi Desert, the fine particle content and fractal dimensions of the soil layer below the root zone of shrub and arbor vegetation increased with increasing stand age, but the trend was reversed in the shallower soil layers. The variability of the soil PSD characteristics was strongly correlated with the variability of the nanopore parameters on the microscopic scale, suggesting that the total pore volume, average pore diameter, and percentage of the micropore volume may be potential indicators of the soil structure and quality.

Background Chronic kidney disease (CKD) is a severe public health problem associated with a disordered gut microbiome. However, the functional alterations of microbiota and their cross talk with metabolism pathways based on disease severity remain unclear. Results We performed metagenomics and untargeted metabolomics in a cohort of 68 patients with CKD of differing severities and 20 healthy controls to characterize the complex interplay between the gut microbiome and fecal and serum metabolites during CKD progression. We identified 26 microbial species that significantly changed in patients with CKD; 18 species changed as the disease progressed, and eight species changed only in a specific CKD group. These distinct changes in gut microbiota were accompanied by functional alterations in arginine and proline, arachidonic acid, and glutathione metabolism and ubiquinone and other terpenoid-quinone biosynthesis pathways during CKD progression. Further metabolomic analyses revealed that the distributions of toxic and pro-oxidant metabolites from these four essential metabolic pathways varied in the feces and serum as CKD progressed. Furthermore, we observed a complex co-occurrence between CKD severity-related bacteria and the characterized metabolites from the four essential metabolic pathways. Notably, Ruminococcus bromii, fecal hydroquinone, and serum creatinine were identified as the main contributors to the integrated network, indicating their key roles in CKD progression. Moreover, a noninvasive model including R. bromii and fecal hydroquinone, L-cystine, and 12-keto-tetrahydro-LTB4 levels classified the CKD severity (area under the curve [AUC]: > 0.9) and had better performance than the serum creatinine level for mild CKD (AUC: 0.972 vs. 0.896). Conclusions Perturbed CKD severity-related gut microbiota may contribute to unbalanced toxic and pro-oxidant metabolism in the gut and host, accelerating CKD progression, which may be an early diagnostic and therapeutic target for CKD.

Steady-state approximation for interpreting NO3 and N2O5 has large uncertainty under complicated ambient conditions and could even produce incorrect results unconsciously. To provide an assessment and solution to the dilemma, we formulate datasets based on in situ observations to reassess the applicability of the method. In most of steady-state cases, we find a prominent discrepancy between Keq (equilibrium coefficient for reversible reactions of NO3 and N2O5) and correspondingly simulated [N2O5]/[NO2]×[NO3], especially under high-aerosol conditions in winter. This gap reveals that the accuracy of Keq has a critical impact on the steady-state analysis in polluted regions. In addition, the accuracy of γ (N2O5) derived by steady-state fit depends closely on the reactivity of NO3 (kNO3) and N2O5(kN2O5). Based on a complete set of simulations, air mass of kNO3 less than 0.01 s−1 with high aerosol and temperature higher than 10 ∘C is suggested to be the best suited for steady-state analysis of NO3–N2O5 chemistry. Instead of confirming the validity of steady state by numerical modeling for every case, this work directly provides appropriate concentration ranges for accurate steady-state approximation, with implications for choosing suited methods to interpret nighttime chemistry in high-aerosol air mass.

Nitryl chloride (ClNO2), an important precursor of Cl atoms, significantly affects atmospheric oxidation capacity and O3 formation. However, sources of ClNO2 in inland China have not been fully elucidated. In this work, laboratory experiments were conducted to investigate heterogeneous reactions of N2O5 with eight saline mineral dust samples collected from different regions in China, and substantial formation of ClNO2 was observed in these reactions. ClNO2 yields, φ(ClNO2), showed large variations (ranging from

Considering the weakness of the conventional physical model of power diodes in the rectifier design, an improved lumped-charge model is presented in this paper to achieve both high accuracy and high efficiency in the simulation of power electronic equipment. To improve the convergence of the physical model simulation in the rectifier circuit, the transmission-line-theory is implemented in the model. Furthermore, a dynamic partition method is implemented in the model to achieve high accuracy of the transient characteristics of the diode during the level transition of the rectifier, with reducing the number of numerical solutions through reallocation of computing resources. Compared with the conventional model, the simulation error is decreased. This successfully adopts the physical model to realize both equipment-level simulation and high accuracy of device simulation in the design of the rectifier and RC snubber circuit. The experimental results show that the error of the proposed model is less than 10% and 5% the minimum, which is a maximum 30% decreasing compared with that of the conventional model. The half-cycle (500μs) simulation of the rectifier circuit with 10 and 20 power diodes cost 18s and 33s.

Aiming at the field management of plum grove in Inner Mongolia of China, taking the dense planting plum groves in Bikeqi town of Hohhot City as the research object, this paper proposed a visual navigation path detection algorithm for plum grove. By processing the video image information of plum grove, comparing RGB and HSV color space model, HSV color model was selected to separate the plant and background in V channel. Homomorphic filtering was used to highlight the region of interest in the image, Otsu was selected to segment the image, the intersection of plum trunk and ground was extracted as feature points, and the least square method was used to fit the navigation path. Through the comparative analysis of detection rate under different detection conditions in one day, the verification test of route accuracy was carried out. The experimental results show that: for dense planting plum grove, the average path detection accuracy of the algorithm is 70% and 73.3% under the condition of front light and weak light, respectively. The detection accuracy and real-time meet the requirements of plum grove field management, and the navigation baseline can be generated more accurately, which provides a preliminary basis for the realization of mechanical vision navigation in plum grove field management.

The internet of satellites (IoS), containing multiple low earth orbit (LEO) satellite constellations, can support tremendous traffic, massive connectivity, and vast coverage. But it also puts forward higher demands for resource management due to the high dynamics of the IoS networks, especially in the malicious jamming environment, where the jammers launch jamming attacks to reduce the efficiency and reliability. Thus, this paper investigates the problem of resource management in malicious jamming environment for IoS, which is divided into three sub-problems: traffic prediction problem, anti-jamming decision problem and resource matching problem. To solve these problems, we proposed a distributed resource management framework (DRMF), which consists of three sub-algorithms. Firstly, the traffic prediction algorithm (TPA) is proposed to deeply mine and accurately predict the traffic rule. Meanwhile, the dynamic anti-jamming algorithm (DAA) is developed to make anti-jamming decision autonomously. Then, based on the outputs obtained by TPA and DAA, the distributed resource matching algorithm (DRMA) is proposed for IoS, and the satellites with insufficient resource can apply for assistance from neighboring satellites with excess resource, thereby improving the safety and efficiency of the entire IoS network. Finally, experiment results and algorithm analysis verify the proposed scheme has better performance than the existing algorithms.