To solve the inevitable shortcomings of single sensor environmental perception system, the LiDAR and camera were fused to combine the advantages of the two sensors and form complementarity for improving the environmental perception capability of unmanned vehicles. The fusion technology of LiDAR and camera was investigated and applied for the target recognition at urban intersections. Combining the search theory of Flood Fill algorithm with the tangent theory of spectral clustering algorithm, and considering the Euclidean distance and spatial distribution characteristics between point clouds, the laser radar target detection method was investigated. The target recognition method based on the fusion of LiDAR radar and camera was proposed, and the traditional PnP solving principle was analyzed. The pose transformation relationship was solved based on the plane normal alignment, and the genetic algorithm was introduced to optimize the solution results. The fusion results of LiDAR and camera were simulated and verified by the autonomous driving simulation software. The results show that by the proposed fusion method of LiDAR and camera, the vehicle targets at urban intersections can be accurately recognized, and the unmanned vehicles can perceive targets within 360° range. This can ensure the safety of unmanned vehicles and improve the environmental understanding ability.

To improve the stability and accuracy of the sound quality evaluation model, the vehicle interior sound quality evaluation model(ALSTM) based on long short-term memory (LSTM) network and attention mechanism was proposed. The steady noise samples of different brands of vehicles at the right ear of drivers under different working conditions were collected, and the subjective evaluation test of noise samples was carried out with annoyance as evaluation index to establish the evaluation data set of interior sound quality. On the basis of the data set, the sound quality evaluation model based on LSTM network was constructed with Mel-scale frequency cepstral coefficient (MFCC) of noise samples as feature input, and the attention mechanism was introduced to optimize the model. The experimental results show that the proposed evaluation model of sound quality can effectively evaluate the vehicle interior noise, and the accuracy in the test set is as high as 97.07%. Compared with other methods, the stability, convergence speed and classification accuracy of the ALSTM model are improved.

To solve the delay problem between sensor perception and V2V communication in the intelligent networked environment, the dual delay multiple lookahead full velocity difference (DDMLFVD) model was proposed with considering dual delay and multiple front vehicle feedbacks. The dual delay information was introduced according to the sensing characteristics of intelligent connected vehicles, and the DDMLFVD model was proposed by combining the multivehicle speed differences and the desired speeds. The tiny perturbation method was utilized to solve the critical stability conditions of the DDMLFVD model, and the effects of the vehicle number in front of ego vehicle and the delay value on the stability domain of the model were investigated. The model was simulated and analyzed by the straight road scenario, and the stability effect of DDMLFVD on traffic flow under variable disturbance and variable delay scenarios was emphatically investigated. The results show that by the proposed DDMLFVD model, the disturbances can be well absorbed in the face of complex disturbances, and the stability of traffic flow can be improved.

To investigate the pressure pulsation characteristics of multiwing centrifugal fans in the rotational stall stage, taking the multiwing centrifugal fans as research object, the Creo software was used to model in three dimensions, and the models were meshed and calculated for unsteady values. Monitoring points were set in different circumferential, radial and axial positions of the impeller outlet, and the internal pressure pulsation law of the fan under the rotational stall condition was analyzed. The calculation results show that the pressure coefficient of the monitoring point at the diaphragm tongue is the largest, and with the decreasing of flow, the intensity of pressure pulsation is increased. The impeller outlet is affected by the dynamic and static interference between impeller and volute, and the amplitude of the pressure coefficient is the largest. When it moves radially to the volute wall, the pressure pulsation degree is weakened. The law of pressure pulsation is slightly affected by the axial position change，while the change of rotational stall degree can cause the change of pressure pulsation. The stronger the rotational stall is, the more intense the pressure pulsation is. The wider the lowfrequency pulsation range is, the higher the stall frequency amplitude is.

To solve the problem of vehicle dynamics state variations in autonomous vehicles due to the uncertainty of road conditions on seacrossing bridges, the vehicle roll control strategy was proposed based on the deep deterministic policy gradient (DDPG) algorithm, and the generalization capability under different speeds was discussed. The vertical model of the seacrossing bridge was constructed to provide dynamic road environment. The vehicle dynamics and vehicle tracking error models were established for incorporating the dynamic characteristics of vehicle roll, sideslip and yaw and for establishing the criteria for roll stability. The state space and action space for the DDPG algorithm were designed, and the reward function was formulated based on the vehicle roll state. The numerical simulation results show that by the DDPG algorithm, good performance is achieved in each episode with robust learning and problemsolving capabilities in complex and uncertain environments. The vehicle roll angle and lateral distance error are ensured within acceptable and minor fluctuation ranges to achieve safe vehicle control.

The robust backstepping sliding mode RBF network adaptive controller was proposed for the quadrotor unmanned aerial vehicle(UAV) attitude system with disturbance. Based on the backstepping sliding mode control, the RBF network was used to approximate and compensate the ideal control law. The minimum parameter learning method of the neural network was adopted, and the weight upper bound of the neural network was estimated as estimated value of the neural network. The adaptation law was used to replace the adjustment of neural network weights, and Lyapunov theorem was used to prove the stability of system. The simulation results show that compared with the backstepping sliding mode control method, the proposed method has shorter adjustment time and better tracking accuracy in the case of disturbance. It is verified that the proposed method has better anti-interference and robustness.

Based on the structure of power battery liquid cooling plate, the structure of the liquid cooling plate was optimized to realize uniform mass flow distribution. Through the Isight optimization platform integrated modeling software of CATIA and the computational fluid dynamics simulation software of STARCCM+, the optimal Latin hypercube method in the experimental design was used to generate sample points in the design space. On the basis, the response surface approximation model of each flow channel mass flow value was established. For the target, the combined optimization strategy of multiisland genetic algorithm and sequential quadratic programming method was used to optimize the response surface model for obtaining the optimal design. The joint model of liquidcooling plate battery pack was established for thermal simulation verification, which was used to verify the established optimization model. The results show that the error between the mass flow distribution of each flow channel of the liquid cooling plate and the target mass flow value is less than 5%, and the maximum temperature of the optimized battery module is reduced by about 2 ℃.

To solve the torque distribution problem of pure electric commercial vehicles driven by dual motors, the fuzzy control strategy based on particle swarm optimization(PSO) was proposed. The physical model of the vehicle power system was established in Simulink/Stateflow software, and the vehicle torque was distributed based on PSO algorithm. Due to the large amount of calculation and being not suitable for the real vehicles, according to the calculation results of PSO algorithm and the traditional project experience, the fuzzy controller with real-time parameter adjustment was designed to carry out torque distribution with high running speed and basically global optimization effect of PSO. The verification and analysis results show that compared with the original single motor power system, the energy consumption of the dual motor power system is reduced by 12.08%. Compared with the average distribution control strategy, the total motor loss is reduced by 13.09% in dual motor power system.

To address the urban distribution route optimization issue in cold chain logistics, the new model of multitemperature codistribution with soft time windows for electric vehicles was proposed for the lowcost and highefficiency needs of logistics enterprises. Based on the cooler and insulated boxes, the different temperaturelevel goods were delivered simultaneously by ordinary electric vehicles for improving vehicle utilization. The improved ant colony algorithm was proposed to solve the problem, and the 2optimization（2opt） algorithm was combined with the ant colony algorithm to enhance the local search capability. The effectiveness of the model and algorithm was verified through case analysis based on the Solomon dataset. The results show that compared to the single temperature distribution, the proposed multitemperature codistribution can reduce delivery costs and improve efficiency. As the width of time windows is expanded, the number of vehicles is decreased, and the delivery cost shows decreasing trend. After the number of vehicles is reduced to the minimum, the total cost continues to decline slowly due to the continuous reduction of incentive costs and spoilage costs.

Based on the real vehicle configuration driven by parallel hub motor, the main factors affecting the braking energy recovery efficiency were analyzed, and the Federal Kalman filter longitudinal speed estimation algorithm was used to propose the three-layer compound braking control strategy. By the braking decision-making layer, the braking conditions were identified and entered into the corresponding braking mode according to the pedal input and driving state. According to the decision, by the braking control layer, the electric braking torque distribution of the front and rear wheels was optimized through particle swarm optimization (PSO) under conventional braking conditions to maximize the effective battery recovery efficiency. Under emergency braking conditions, the fuzzy self-tuning PID algorithm was used to realize the safe and effective control of the slip rate of each wheel on different adhesion roads, and the robustness of the anti lock braking control was optimized with improved braking safety. The command of the control layer was responded by the braking executive layer, and the electric braking compensation mechanism was designed to quickly compensate and adjust the pressure error of each wheel cylinder for improving the braking stability of the vehicle. The control strategy was verified by the real vehicle.

To further improve the cooling performance and operation stability of system, the gasliquid separator enhanced loop thermosyphon/vapor compression hybrid system was proposed. In the thermosyphon mode, the separator was used to reduce the pressure drop of fluid in the twophase tube for improving the performance of steadystate heat transfer and startup and the operation stability of system. In the vapor compression mode, the separator was used to make the saturated vapor into the condenser for increasing the mass flow rate in the condenser and enhancing the system coefficient of performance (COP). The simulation model of the hybrid system was developed to investigate the impact of structural parameters on thermosyphon mode performance. The difference of refrigeration performance between the new system and the conventional system in vapor compression mode was comparatively analyzed under the given structural condition of system. The results show that the system performance is the best when the inner diameters of twophase tube and gas tube are respective 10 mm and 9 mm with separator height and total system height of respective 1.05 m and 1.45 m. Compared with the conventional system, the new system can achieve better performance under any operating condition, and the maximum COP of the new system is improved by 19.70%.

The connection fault characteristics of parallel battery modules were analyzed by the designed connection fault testing method， which showed that the larger the connection fault resistance was, the larger the terminal voltage deviation was. The voltage and branch current characteristics at different connection fault degrees were explored by the fault simulation model. The results show that the closer the fault location to the load connection point is, the smaller the impact on the uneven distribution of each branch current is. The characteristics of voltage, current and incremental capacity (IC) curves at different connection fault degrees indicate that there are significant differences in the branch current. The characteristics of the IC curve vary greatly at different C-rates. The peak Ⅱ is relatively stable, which has correlation with the connection resistance.

To solve the problems of privacy leakage and data tampering in the storage and management of user identity data on existing centralized crowdsourcing logistics platforms, the trusted identity authentication model based on the unspent transaction output(UTXO) model blockchain was proposed. The implementation of identity registration, identity verification, identity revocation and identity authentication was realized in the model by blockchain technology. To solve the risks associated with storing user privacy data on the blockchain, the hybrid encryption and decryption scheme was adopted to ensure the secure storage and sharing of personal information on the chain. For the issue of platform user identity authentication, the trusted identity credential was used to log into the crowdsourcing logistics platform by the zero-knowledge proof method. The security of identity information and authentication was analyzed, and the security performance of the proposed method was compared with those of the LIU and LI methods. The experiments were completed in Python 3.9 environment, and the identity information encryption and zero-knowledge identity authentication were tested and analyzed. The experimental results show that the authentication latency and transaction processing capability of the proposed scheme are superior to those of the existing SHAO scheme.

The reaction kinetics mechanisms of KAUST PAH mechanism 2（KM2） and AramcoMech are commonly used to simulate and predict the generation of polycyclic aromatic hydrocarbon(PAH) and soot. The two kinetics mechanisms were evaluated based on the flame multiparameter measurement data using methane mixed with ethylene opposed diffusion flame as research carrier. The results show that the two mechanisms can accurately predict the thermochemical structure of methane mixed with ethylene diffusion flame. However, the prediction performance of the influence of methane mixing on PAH and soot generation is poor. Neither mechanism fails to predict the inhibition of PAH and soot generation in methane mixed with ethylene flame. Comparing the similarities and differences of PAH and soot generation paths between the two mechanisms, it is found that both mechanisms overestimate the sensitivity of propargyl radical（C3H3·）generation to methane blending. The flame multiparameter measurement data are helpful to further optimize and verify the reaction mechanism of methane fuel.

To solve the problem of excitation trajectory design in the six-axis robot dynamic parameter identification, the improved differential evolution (IDE) algorithm was proposed to optimize the excitation trajectory parameters. The dynamic model of the six-axis robot was established by Newton-Euler iteration method, and the condition number of the minimum inertia parameter observation matrix was taken as optimization objective function. By improving the differential evolution algorithm, the inverse optimal worst strategy was introduced to improve the initial value of the population, and the adaptive algorithm was used to improve the mutation factor and crossover factor. The improved differential evolution algorithm was used to optimize and design the Fourier series for meeting the constraints of robot and as excitation trajectory for identifying the parameters of robot. The experimental results show that the excitation trajectory designed by the proposed optimization method can give full play to the dynamic characteristics of robot and improve the anti noise ability of robot dynamic parameter identification experiment, which can provide foundation for establishing accurate robot dynamic model.

To solve the problem of low message authentication efficiency in the internet of vehicles (IoVs) caused by limited network bandwidth and computing power, the certificateless batch authentication scheme applied to the IoVs was proposed. The authority generated evaluation proof for each registered vehicle and calculated global commitment based on all registered vehicles. The vehicles participated in the message authentication process based on the evaluation proof. In the authentication process, the global commitment could be utilized by the trusted authorities to check the validity of vehicle evaluation proofs through bilinear pairing for realizing rapid batch authentication. For the privacy and security, the zeroknowledge proof was introduced by the proposed scheme based on the blockchain architecture, which provided privacy protection properties of anonymity and unlinkability for vehicles. The accurate traceability and fast revocation were realized through the blockchain state database. The security analysis, computational efficiency analysis and simulation experiments were conducted. The results show that the proposed scheme can satisfy various security requirements of anonymity and unlinkability, and the average authentication time is 0.357 ms, which is at least 12.46% better than those of existing schemes.

To solve the safety problem of automobile side collision, the safety performance of the driver side door was optimized and analyzed by combining simulation analysis and mathematical optimization with passenger car door as research object. According to the test requirements of GB/T 37337—2019 of protection of the occupants in the event of a lateral pole collision, the lateral pole collision simulation model was established, and the reliability of the simulation model was analyzed. The safety performance indicators of the intrusion of the side wall of vehicle, the energy absorption of each component of the door and the acceleration of vehicle were investigated. By designing the sample data of the Latin experiment, the response surface model of the optimization target was established, and the multiobjective optimization solution of the established optimization mathematical model was obtained based on the nondominated sorting genetic algorithmⅡ（NSGAⅡ）. The Euclidean distance of the Pareto front solution was calculated by the entropyTOPSIS method, and the final optimization scheme was determined. The results show that when the door mass is reduced by 1.22%, the maximum intrusion of the door is reduced by 10.28%, and the proportion of energy absorption of the main energyabsorbing parts of the door is increased by 16.14%, while the peak acceleration of the door is reduced by 7.58%. The optimized door is improved to different degrees in terms of intrusion, acceleration and energy absorption, which can enhance the door impact intensity and improve the side impact safety of car. The peak acceleration of the vehicle is reduced by 16.37%, and the secondary impact force on the occupants is relieved.

To solve the poor performance problem of the existing solutions based on filters, dynamic analysis and static analysis in detecting unknown XSS attacks, the machine learning methods were used to efficiently detect unknown XSS attacks, and the XSS attack detection model was proposed based on genetic algorithm and support vector machine. The fuzzy testing was used to generate XSS attack pre samples, and the genetic algorithm was used to search the feature space. The optimal test cases were iteratively generate, and the dataset was expanded to enrich the XSS attack vector library. The attack detection model was proposed based on genetic algorithm and support vector machine, and the coding rules for XSS test cases were determined. The fitness function was designed, and the designs of selection operator, crossover operator and mutation operator were completed. The detection performance of the classifier was evaluated based on accuracy, recall, false positive rate and F1 score. The results show that the accuracy of the model can reach 99.5%. Compared with other detection methods, the proposed detection model has better detection performance with high recall rate and low false positive rate.

To investigate the photo-thermal conversion performance of TiN nanofluid in the direct absorption solar collectors（DASC）, the ethylene glycol (EG) based TiN nanofluid with polyacrylic acid (PAA) stabilizing ligand was prepared. The long-term stability of the TiN-PAA nanofluid was characterized by conducting the static storage test at ambient conditions to show that the average transmittance of the TiN-PAA nanofluid with 100 mg/L was increased by 1.908% after ambient storage for 310 days. The photo-thermal conversion performance of the TiN-PAA nanofluid in DASC was evaluated. The effects of nanoparticles concentration, mass flow rate and irradiation intensity on photo-thermal conversion performance of the collector with TiN-PAA nanofluid were discussed. The results indicate that the stagnation temperature and efficiency of the TiN-PAA nanofluid are much higher than those of the base fluid EG after 2.0 h irradiation, and the stagnation temperature of TiN-PAA nanofluid with 100 mg/L is 80.12 ℃. The efficiency of TiN-PAA nanofluid is improved with the increasing of mass flow rate, and the efficiency of 75 mg/L TiN-PAA nanofluid is increased by 32.69% as the mass flow rate is increased from 1 mL/min to 3 mL/min. However, the efficiency of TiN-PAA nanofluid is decreased as the irradiation intensity is increased. The efficiency of the 75 mg/L TiN-PAA nanofluid reaches 69.10% when the irradiation intensity is 800 W/m2.

 For the system log data with the distribution characteristics of "group anomaly" and "local anomaly", traditional semi-supervised log anomaly detection method of anomaly detection with partially observed anomalies(ADOA) has poor accuracy of pseudo-labels generated for unlabeled data. To solve the problem, the improved semi-supervised log anomaly detection model was proposed. The known abnormal samples were clustered by k-means, and the reconstruction errors of unlabeled samples were calculated by kernel principal component analysis. The comprehensive anomaly score of sample was calculated from reconstruction error and similarity to abnormal samples, which was used as pseudo-label. Sample weights for the LightGBM classifier were calculated based on pseudo-labels to train the anomaly detection model. The impact of the proportion of training set samples on model performance was explored through parameter experiments. The experiments were conducted on two public datasets of HDFS and BGL. The results show that the proposed model can improve the pseudo-label accuracy. Compared to existing models of DeepLog, LogAnomaly, LogCluster, PCA and PLELog, the precision and F1 score are improved. Compared to traditional ADOA anomaly detection methods, F1 scores are increased by 8.4% and 8.5% on the two datasets, respectively.

Steel slag was used to replace some basalt coarse aggregates in porous asphalt mixtures, and the laboratory tests and numerical simulations were conducted to investigate the fatigue performance of steel slag porous asphalt mixtures. Based on the CT scanning image, the steel slag, basalt, asphalt mortar and pores in the mixture were differentiated by threshold segmentation and imported into particle flow code（PFC） to establish discrete element model of steel slag porous asphalt mixture with particle diameter of 0.4 mm. Based on the macro-meso mechanical parameter conversion and parameter correction through virtual strength test, the virtual fatigue damage numerical simulation was carried out to obtain the specimen microcrack propagation path and fatigue life under repeated load. The results show that the virtual strength test can well fit the peak load and displacement curve of steel slag porous asphalt mixture specimens. The crack propagation path of the specimens conforms to the actual fracture situation, and the distribution of cracks mainly propagates along the internal pores of the model. The fatigue damage model can depicte the trend that the parallel bond diameter is decreased with the increasing of load cycle and decreased acceleratingly with the increasing of tensile stress between parallel bond contacts. The results of the virtual fatigue damage model are relatively close to those of laboratory fatigue damage test with an error of less than 10%, indicating that it can effectively predict the fatigue life.

To investigate the hydrodynamic cavitation phenomenon in microchannel devices, the influence laws were analyzed from three aspects of geometric shapes and dimensions of flow restrictive elements, working fluids and channel roughness. The common shapes of flow restrictive elements are mainly microorifice, microventuri, microdiaphragm and micropillar. Different geometries of flow restrictive elements have different cavitating flow characteristics. The dimensional parameters of the flow restrictive elements and the scale effect caused by the size reduction play an important role in the cavitating flow patterns. The commonly used working fluids contain deionized water, ethanol, phosphatebuffered saline (PBS), refrigerant (R123), poly(vinyl alcohol) (PVA)，microbubbles (MBs) suspension, perfluoropentane (PFC5) suspension, titania nanoparticle suspension, binary liquid mixtures, etc. Compared to water, the remaining working fluids can increase cavitation intensity to different degrees. The introduction of roughness is mainly realized by the surface roughness elements and sidewall roughness elements of the channel, and the cavitation intensity can be significantly improved compared to that of the smooth surface. Microchannel hydrodynamic cavitation devices are mainly used in energy harvesting, liquidphase exfoliation and biomedical fields. Based on the research status of microscale hydrodynamic cavitation at home and abroad in recent years, the potential research directions and application trends of hydrodynamic cavitation phenomena in microchannel devices are provided.

 To solve the severe miss detection problem in pedestrian detection caused by insufficient pixel information of distant targets and occlusion induced loss of human pattern information, the pedestrian detection method was proposed based on dual key points combinations. The discriminative semantic features of pedestrians were effectively extracted and fused by utilizing key points of the head and center regions for significantly reducing the pedestrian miss detection rate. The deformable convolution was introduced into the deep aggregation backbone feature network to enlarge the receptive field and enhance the semantic information of human pattern. The dual branch joint detection module based on key points combinations was designed, and the positive samples for different branches were redefined to strengthen the semantic information of small scale and occluded targets. The results of the dual branch detection were fused using the non maximum suppression (NMS) algorithm. The results show that on the CityPerson validation dataset, the average miss detection rates of the normal, small scale and heavily occluded subsets reach 8.24%, 11.81% and 30.59%, respectively. Especially, for the heavily occluded subset, the miss detection rate is reduced by 15.71% compared to the traditional method ACSP. By the proposed method, the detection speed reaches 16 frames per second. On the CrowdHuman dataset, the average precision and average miss detection rate are 86.30% and 45.52%, respectively. Compared with other state of the art methods, the proposed method exhibits superior performance in average precision, miss detection rate and detection speed, which demonstrates significant application value in complex scenarios with dense pedestrian crowds.

The crystal structure, electronic structure and the effect of spinorbit coupling (SOC) on the magnetic and electronic properties of double perovskite Ba2CoOsO6 were systematically investigated by firstprinciples calculations. Through the coupling analysis of electron structure and spin orbit, the influence on the macroscopic physical properties was discussed. The results show that Ba2CoOsO6 is antiferromagnetic semiconductor material with narrow band gap and disordered Os1/Co1 defects. The electronic structure analysis confirms that the charge combination of Ba2CoOsO6 is Ba2+2Co2+Os6+O2-6, which verifies the existence of Co2+ and Os6+. The spin magnetic moment of Co and Os ions is decreased by SOC, and the band gap is narrowed. The results of firstprinciples calculations show that the effect of spinorbit coupling on electronic structure and electromagnetic properties can not be ignored. Compared with other methods, the calculated values are closer to the results of experimental analysis, which can verify the theoretical calculation.

To solve the problem that the previously constructed Sboxes based on chaotic systems were difficult to achieve good cryptographic performance, the design scheme for Sboxes was proposed based on hyperchaotic system and genetic particle swarm optimization algorithm. Introducing sine and cosine functions and exponential factors, the twodimensional hyperchaotic system was constructed based on the onedimensional chaotic mapping. The performance analysis was conducted by system bifurcation diagram, phase diagram and Lyapunov exponent diagram to reveal that the chaotic system exhibited continuous hyperchaotic intervals in the parameter range with complex chaotic behavior. By varying initial values, parameters and iteration times of the chaotic system, Sboxes were dynamically generated. Combining particle swarm optimization algorithm and genetic algorithm, the genetic particle swarm optimization algorithm for Sboxes was proposed, and the Sboxes generated by chaotic system were used as initial population. The particle swarm optimization algorithm was leveraged to enhance the crossover operation in the genetic algorithm, and a new mutation strategy was introduced in conjunction with hillclimbing algorithm. To verify the performance of the generated Sbox, the simulation tests were conducted on bijective property, nonlinearity, strict avalanche criterion, differential probability and bit independence criterion. The simulation results show that the proposed optimization algorithm can generate Sboxes with good performance in terms of nonlinearity, differential uniformity and bit independence criterion.

To explore the characteristics of traffic volume data and improve the accuracy of parameter estimation for traffic volume data fitting, the parameter estimation method for traffic volume data fitting was proposed based on the maximum correlation entropy criterion. The traffic volume data was preprocessed, and the probability distribution of the data was specified. The maximum correlation entropy criterion was used to estimate the model parameters, and the gradient ascending method was used to output the model parameters. The estimated values of the probability distribution parameters were obtained, and the results were compared with those of the traditional methods. Based on the measured data and the four assumed distribution models of Normal distribution，Logarithmic normal distribution，Webull distribution and Rayleigh distribution, the performance evaluation was conducted.The results show that the proposed method has the best fitting performance, and the comprehensive evaluation indexes are respective 0.979 00, 0.726 08, 1.397 69 and 1.494 50, with strong accuracy and reliability.

Under nitrogen atmosphere, the thermogravimetric tests were carried out on rape straw, polypropylene, acrylonitrile butadiene styrene resin (ABS resin) ， rape straw/polypropylene  with mass ratio of 1∶1 and rape straw/ABS resin with mass ratio of 1∶1 at temperature range of 30-800 ℃ with heating rate of 20 ℃/min. The apparent activation energy was measured by Friedman method， and the kinetic analysis was also carried out. The catalytic pyrolysis of rape straw and the cocatalytic pyrolysis of rape straw/polypropylene and rape straw/ABS resin were tested, and the threephase yield was calculated and analyzed. The results show that there is interaction among the copyrolysis reactants, and blending is conducive to reducing the difficulty of pyrolysis reaction and improving the spontaneity of reaction. According to the GC/MS analysis, the highest yield of monocyclic aromatic hydrocarbons is about 22.11% when the blending ratio of straw and polypropylene is 1∶1, and the highest yield of monocyclic aromatic hydrocarbons reaches 32.87% when the blending ratio of straw and ABS resin is the same.

To investigate the stress distribution and safety reliability of the baffle exposed to high temperature in heater, the stress of baffle was analyzed based on the finite element method. The dangerous section path was selected for the stress linearization analysis, and the structure of baffle was optimized according to the safety assessment results. The results show that the stress concentration caused by sharp angle has great influence on the stress distribution of   baffle, and the excessive film stress of PL is the main reason of stress assessment risk. The overall stress of baffle is increased with the increasing of fuel inlet velocity, and the temperature is positively correlated with the thermal stress. The stress change trend is similar at different speeds, and the structure discontinuity at the connection of heat exchange tube bundle and folding plate can cause stress increase. In the optimization scheme, the maximum thermal stress of baffle is reduced by 16.7% when the sharp angle is passivated. The maximum thermal stress with thermal barrier coating is decreased by 44.5% as compared with that for passive tip angle, and decreased by 53.7% as compared with that for the original structure. The overall thermal stress is decreased, and the strength of the optimized baffle plate is assessed as qualified.

The novel Fe doped carbon dots (FeCDs) with good photoluminescent properties were rapidly synthesized by microwaveassisted method with ferric citrate and polyethyleneimine as raw materials. The results show that the FeCDs have good stability under the conditions of broad pH range from 3 to 9, high ion strengths and long storage time. The prepared FeCDs can easily penetrate and accumulate into the cell, which can be used as effective probe for biological imaging of A549 cells. The FeCDs display distinct peroxidaselike (POD) activities, which is evidenced by the catalyzed oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) into blue oxidized products in the presence of H2O2. A colorimetric method for the determination of H2O2 is established based on the results. Under optimal reaction conditions, the colorimetric method for H2O2 detection is constructed with good linear relationship from 0 to100.00 μmol/L and detection limit down to 0.13 μmol/L. The highly selective and ultrasensitive method is successfully applied to detect H2O2 inside A549 tumor cells with good recoveries of 98.4%-102.8%, which can verify the proposed method.

To solve the problems that the current segment-based video abnormal event detection model with only considering the normal or abnormal of segments could lead to the abnormal fragmentation, the abnormal start/stop location spreading and the difficulty of abnormal event classification, the video abnormal event detection and classification method was proposed based on salient features and space-time graph network. The video anomaly temporal fragment integration and refinement method based on space-time fusion graph network was proposed to integrate the continuous anomaly regions. Considering the feature transferability of the space-time fusion graph network, the anomaly regions were refined to effectively solve the problems of uncertainty in anomaly discrimination and fragmentation of anomaly fragments. To solve the difficult classification problem of weakly supervised anomalous events caused by the intrinsic features of difficultly effective expressing, the abnormal event feature learning and classification method was proposed. The feature similarity graphs and abnormal similarity graphs were established in abnormal regions, and the graph convolutional networks were used for the abnormal event feature fusion learning. The abnormal-imbalance loss function was designed to improve the classification performance of abnormal events. The results show that by the proposed method, the AUC reaches 85.37% on the UCF-Crime dataset, which is 9.83% higher than the baseline of SULTANI method and 0.89% higher than that by the state-of-the-art method of THAKARE method. For the abnormal event classification, the average accuracy of 74.06% is achieved, which is increased by 4.39% compared with the existing ZHOU method. The proposed method can detect and locate video anomalous events more effectively with better anomalous event classification performance.