Abstract:Global climate change caused by excessive greenhouse gas (GHG) emissions has been widely concerned.Agricultural activities are the second largest source of GHGs emissions,so it is urgent to reduce agricultural GHGs emissions.Biochar,which has stable properties,abundant aromatic carbon and pores,is produced by pyrolysis of biomass under high temperature and limited oxygen conditions.The effect of biochar amendment on GHGs mitigation and soil carbon sequestration is excellent,and biochar application has the potential to participate in China's ongoing carbon trading of voluntary emission reduction (VER).However,the factors affecting the carbon sequestration and GHGs emission reduction effect of biochar are complicated,so it is necessary to systematically summarize the mitigation effect,influencing factors and research progress of biochar.This paper reviewed researches on the GHGs emission reduction and carbon sequestration effect of biochar through pot and field experiment as well as meta-analysis research.At the same time,CiteSpace software was used for visual analysis to explore the research hotspots and development trends in this field.The opportunities and challenges faced by biochar application projects participating in carbon trading were summarized based on the characteristics of domestic and foreign carbon trading market development and corresponding supporting policies.Corresponding solutions were also provided in this study,which offered scientific guidance and useful reference for the development of carbon sequestration and GHGs emission reduction research of biochar and the successful participation of biochar application projects in carbon trading.

Abstract:To explore the blocking impact of plant communities on atmospheric particulate matters,we monitored the PM_2.5 and PM₁₀ concentrations and related meteorological factors near plant communities in three functional areas of Zhengzhou's Jinshui district,which were garden area (Digital Park),residential area (Zhengzhou Blue Bay),and cultural & educational area (Henan Agricultural University).The monitoring period covered a whole winter from December 2020 to February 2021.The results indicated that the diurnal variation trends of PM_2.5 and PM₁₀ concentrations were basically the same for all sampling plots,which were generally high in early morning and low in evening;obvious differences in PM_2.5 and PM₁₀ concentrations were observed among plant communities,which were most significant between the square plot and other plots;for the three functional areas,the blocking of PM_2.5 and PM₁₀ were all the strongest by the combined structure of arbor,shrub and grass,followed by arbor & shrub and arbor & grass structures,and the lowest by structure of shrub & grass and single structure of grass;the PM_2.5 and PM₁₀ concentrations were observed to be negatively correlated with temperature and wind speed,and positively correlated with relative humidity.

Abstract:To explore the impact of climate change on the lake area of Selin Co,this paper used maximum likelihood method to extract the lake areas of Selin Co for the past 33 years (1988-2020) from Landsat data,then analyzed the variations in lake area,temperature,precipitation as well as snow cover depth in Selin Co basin using linear regression and Mann-Kendall test,and discussed the correlation between lake area and climate change by Pearson correlation.The results showed that,in the past 33 years,the Selin Co Lake expanded by 650.70 km² at the rate of 203.34 km²／(10 a),mostly at northward and southward directions.The average annual temperature and precipitation increased significantly at the rates of 0.50 ℃／(10 a) and 17.32 mm／(10 a) (p<0.05),while the average maximum snow cover depth decreased significantly at the rate of 0.65 cm／(10 a) (p<0.05) during 1988-2020.An extremely significant correlation was found between the change of lake area and the rise of air temperature in the basin as well as the decreasing maximum snow cover depth in cold season (p<0.001),indicating that the Selin Co lake's expansion in the past 33 years was a consequence of the increasing water supply from ice-snow meltwater due to the rising air temperature in Selin Co basin.

Abstract:Here,a hierarchical autoregressive spatio-temporal model under the Bayesian framework is proposed to address the simultaneous multi-site PM_2.5 prediction.The true daily average concentration of PM_2.5 is regarded as a potential spatio-temporal process,then the temporal correlation is described by the first-order autoregressive process and the spatial correlation is captured based on the Matérn process,which greatly improves the efficiency in dimension reduction and synchronous prediction.In addition,meteorological factors such as daily maximum temperature,relative humidity and wind speed are used as explanatory variables to improve the prediction accuracy.The combination of Bayesian method and MCMC can realize parameter estimation and prediction process due to the model's hierarchical structure.The empirical analysis of daily PM_2.5 concentration in Beijing shows that the proposed model has good interpolation or prediction performance in both spatial and temporal dimensions.

Abstract:In view of the problems that aspect level sentiment analysis tasks cannot give full consideration to syntactic comprehensiveness and semantic relevance,and the graph volume used in most studies only considers the top-down dissemination of information and ignores the bottom-up aggregation of information,this paper proposes a sentiment analysis model based on attention and dual channel network.While expanding the dependency representation,the model uses self attention to obtain the information matrix with semantic relevance,and uses a dual channel network to combine comprehensive syntactic and semantic relevance information.The dual channel network focuses on the semantic features of top-down propagation and the structural features of bottom-up aggregation respectively.The graph convolution output in the channel will interact with the information matrix,pay attention to complement the residual,and then complete the tasks in the channel through average pooling.Finally,the final sentiment classification features are obtained by the fusion of semantic based and structure based decision-makings.The experimental results show that the accuracy and F1 value of the model are improved on three public data sets.

Abstract:The process of screening and developing new drugs through experiments is very slow and requires a lot of manpower and material resources,and the use of computer-aided prediction of the molecular properties of drugs can greatly save time and cost of drug development.Therefore,in order to enable anti-breast cancer candidate drugs to have good biological activity and ADMET properties for inhibiting ERα,the random forest classifier was first used for the collected 1 974 compounds to screen the top 20 molecular descriptors with the most significant effects on biological activity.Then a QSAR model was established using this and pIC50 value as characteristic data.The biological activity values of 50 new compounds were predicted via the PSO optimized BP neural network,with the model fit of 0.833 7 and the root mean square error of 0.731 5,which were more consistent with the actual values than the predicted results of the BP neural network.Subsequently,in order to improve the success rate of drug development,the ADMET classification prediction model was constructed using PSO to optimize the SVM based on the existing ADMET property data.The algorithm cross-validation CV accuracy rate reached 94.076 7％,and the prediction accuracy rates of the five index models were all above 79％.The results show that the proposed model has better prediction performance than the benchmark model,and the adopted prediction strategy is effective,which can provide reference for the discovery and development of anti-breast cancer drugs.

Abstract:Introducing regularization into the correlation filter tracking algorithm can effectively improve the tracking efficiency,but it takes a lot of effort to adjust the predefined parameters.In addition,the target response occurring in non-target areas will lead to tracking drift.Therefore,an Automatic Global Context Awareness Correlation Filter (AGCACF) tracking algorithm is proposed.First,during the tracking process,the automatic spatial regularization is realized using the target local response change,then its module is added into the target function to enable the filter to focus on the learning of the target object.Second,the tracker utilizes the global context information of the target,which can avail the filter learn more information related to the target and reduce the impact of background on tracking performance. Then a temporal regularization term is added to the filter to fully learn the change of targets between adjacent frames to obtain more accurate model samples.Experimental results show that the proposed AGCACF tracking algorithm has better tracking effect in distance accuracy and success rate compared with other tracking algorithms.

Abstract:In view of the increasing concern on model efficiency in computer vision,this paper proposed several optimization schemes to improve the flame detection models in model efficiency as well as the detection performance.A backbone network (FIRE-Net) was constructed from a multi-convolution combined structure,which can efficiently extract rich flame features from multiple scales.Then an improved weighted bidirectional feature pyramid network (BiFPN-mini) was used to quickly achieve multi-scale feature fusion.In addition,a new attention mechanism (FIRE-Attention) was proposed to make the detector more sensitive to flame characteristics.The above optimizations were combined to develop a new flame detector abbreviated as FIRE-DET,which was then trained on self-built dataset and tested on internet videos.The experimental results showed that the FIRE-DET outperformed mainstream algorithms by its flame recognition accuracy of 97％ and frame rate of 85 FPS,thus provides a more common solution to solve the flame detection.

Abstract:In the past few years,robots have become an important means of substation inspection,and robotic inspection technology for non-fixed lines has received increasing attention in order to perform inspection tasks more flexibly.How to achieve high-precision positioning in complex substation environment is one of the core problems to be solved.It is difficult for a single sensor to meet the requirements of reliable positioning in substations,therefore,this paper designs a multi-sensor fusion LINS-GNSS positioning method.Its front-end tightly couples LiDAR and inertial navigation based on an iterative error-state Kalman filter framework,which recursively corrects the estimated state by generating new feature correspondences in each iteration.The back-end uses a factor graph optimization approach to loosely couple the localization results from the satellite navigation with the localization results output from the LINS back-end.The optimization process first aligns the local coordinate system with the global coordinate system,then adds the position constraints of the GNSS as a priori edge to the factor graph in the back-end,and finally outputs the positioning results in the global coordinate system.In order to evaluate the performance of the LINS-GNSS system in the substation environment,this paper conducted field tests under real scenarios.The experimental results show that the LINS-GNSS system can achieve a positioning accuracy better than 0.5 m in the substation environment,better than LIO-SAM.

Abstract:Aiming at the collaborative optimization of multi-UAV reconnaissance and communication service for multiple heterogeneous targets,the Stackelberg game model is constructed by considering the mission requirements and target values,as well as the restriction between multi-UAV coordination gain and task behavior.The upper-level drone is established as the leader of the game,while the lower-level drones are established as the followers of the game,and then a distributed strategy update iterative algorithm is proposed,which realizes the stable convergence of the multi-UAV task allocation scheme and the optimization of the task revenue.Simulation results show that the proposed approach can effectively improve the efficiency of multi-UAV systems to complete multiple tasks at the same time,and can achieve efficient collaboration for the values of heterogeneous tasks in different environments.

Abstract:In order to timely identify the changing trend of marine environment and reduce the influence of long-term accumulated marine environment data on prediction model,an online prediction model of marine environment data based on recurrent online sequential extreme learning machine (R-OSELM) is proposed.The marine environment data training set is initialized by an online method,the existing marine environment data is input block by block via online sequential extreme learning machine algorithm,and the input weight is cyclically processed by automatic coding technology of extreme learning machine and a normalized method,which realize the online update of the prediction model.Finally,online prediction of marine environment data is completed.The model is then used to predict dissolved oxygen,chlorophyll A,turbidity,and blue-green algae.The results show that the prediction accuracy of R-OSELM model is better than that of the comparison model.It is verified that the proposed R-OSELM model is capable of online prediction of marine environment data,which can provide support for early warning of marine eutrophication and other marine environmental pollution.

Abstract:This paper proposed a Marching Trapezoidal Polyhedrons 3D modeling algorithm (MTPD) based on the cone-shaped spatial distribution of Doppler weather radar base data.In this algorithm,a trapezoidal polyhedron was introduced to replace the cube in the conventional modeling algorithm as a basic volume element for modelling.On the other hand,the hexahedral index or tetrahedral index was selected as the construction model for the 3D iso-surface based on the difference in spatial range for 3D radar modeling,to balance the efficiency of the algorithm and the precision of the modeling results.Based on this algorithm,a Doppler weather radar 3D visualization platform was developed using WebGL technology.The results revealed that the algorithm significantly improved efficiency without compromising the precision of 3D modeling when compared with the conventional modeling algorithm based on radar grid data.The durations for the algorithms were reduced by 1.9 seconds and 0.7 seconds,respectively under the hexahedral index mode and the tetrahedral index mode,while under the tetrahedral index mode,the 3D echo structure was more continuous with a higher level of precision.The Doppler weather 3D radar visualization platform based on the B／S architecture could provide a cross-platform 3D radar display,thus visualize the 3D structure of convective cloud effectively.

Abstract:The operation experiences have shown that the large-scale DC magnetic bias caused by DC grounding electrode can be attributed to the uneven surface potential distribution.Here,the Chebyshev polynomial is used to fit the Hankel transform kernel function in order to solve the surface potential distribution for the complex earth model of wide area depth stratification.The adaptive order fitting method of Chebyshev polynomial for kernel function is obtained via shift operation,coefficient expansion and truncation error determination,which greatly reduces the calculation difficulty of surface potential distribution in a large area caused by DC grounding electrode.Compared with the standard grounding calculation software CDEGS,the proposed Chebyshev polynomial approach achieves less than 1 V of earth surface potential deviation in range of 1-100 km when the DC grounding current is 5 000 A.Moreover,the order of the Chebyshev polynomial has influence on the solution results,and it is confirmed that the 20th-order Chebyshev polynomial can meet the accuracy requirements for general DC bias risk assessment.The proposed surface potential assessment method based on shifted Chebyshev polynomial provides a basic technical means for the risk assessment of DC bias,which is helpful to reduce the difficulty of DC bias risk assessment for power grid.