In response to the current problems of organic chemistry basic experiments are outdated and lack of systematization, we strengthen the exploration of experimental teaching "gender once". In this paper, an innovative comprehensive organic chemistry experiment for the dimethylation of 6-amino-1, 2, 3, 4-tetrahydro-1-naphthoneone was designed. The substitution reaction was carried out under alkaline conditions with iodomethane as the methylation reagent to generate dimethyl substitution products, and the products were structurally characterized by nuclear magnetic resonance hydrogen spectroscopy (1H NMR), nuclear magnetic resonance carbon spectroscopy (13C NMR), and high performance liquid chromatography. This experiment contains learning contents such as literature search, experimental design, experimental operation, sample characterization and data analysis, and students can improve both theoretical knowledge and practical operation to enhance their overall quality.

A method was developed for the separation of total flavonoids, total saponins, anthocyanins and polysaccharides from Aronia melanocarpa, and their antioxidant activity, inhibition of tyrosinase

activity and inhibition of α-glucosidase activity were investigated also. Using quality of target components as evaluation index, the optimal extraction parameter of total flavonoids, total saponins, anthocyanins and polysaccharides were ascertained as follows, the most suitable solvents as anhydrous ethanol, 60% ethanol, 60% ethanol and water, the absorption rate as 1.5, 2.5, 2.5, 2.5 mL/g, and the soaking equilibrium time as 2, 2, 2, 1 h, respectively. IC50 value was used as index to evaluate the activity difference in vitro between each component. The results showed during the first round of cyclic extraction the extraction rates of total flavonoids, total saponins, anthocyanins and polysaccharides were 76.09%, 84.38%, 73.75% and 72.23%, which that of all components reached more than 90% during the fourth round. The concentration of total flavonoids, total saponins, anthocyanins and polysaccharides in crude products were 8.93±0.16 mg/g, 70.39±0.26 mg/g, 1.51±0.02 mg/g and 695.46±1.39 mg/g, respectively, and they were purified to the concentration of 30.29±0.24 mg/g, 252.78±0.73 mg/g, 3.66±0.24 mg/g and 884.62±2.03 mg/g, respectively. Experiments on activity in vitro indicated that all the components had certain antioxidant activity, tyrosinase activity inhibition and α-glucosidase activity inhibition ability. Column chromatographic method combined with cyclic extraction could separate efficiently multiple active components which all possess different activities in vitro. The research results provided experimental basisand technical support for Aronia melanocarpa in food development and utilization field.

Aim at the characteristics of mixed oils with more components and large interference fromimpurities,A method for the determination of lead in complex oil samples by graphite furnace atomic absorptionspectrometry with pressure microwave digestion was developed.The optimum conditions of graphite furnacewere obtained that the matrix modifier was 3 μL mixed solution of NH?H?PO?(1%)/Mg(NO?)?(0.05%),theash temperature set to 550 ℃ and an atomization temperature set to 1700 ℃.The results of performance testwere as follow:in concentration range of 0~80 μg/L,the linear equation is y=0.0028x-0.0002 which r2was 0.9979,and the range of standard recovery rate was 95.5%～103.4%,and the relative standard deviation(RSD%)was 0.31%~2.13%(n=5).These results of performance test showed that the built method had agood linear relationship with high accuracy and precision.The proposed method was used to analyze the leadcontent of 8 oil samples,and the practical results showed that the proposed method can accurately determinethe content of Pb element in complex oil samples.

Fe82Zr7B10Cu1 and Fe81Zr7Mo2B10 amorphous ribbons were prepared by the single by single roll rapid quenching method and isothermally annealed at different temperatures. The thermal behavior, microstructure and magnetic property were investigated by STA, XRD, TEM and VSM. Two kinds of amorphous alloys exhibited different crystallization processes. The precipitated phase was only α-Fe phase in the initial crystallization stage of Fe82Zr7B10Cu1 alloy. Both α-Mn type and α-Fe phases were observed in the initial crystallization stage of Fe81Zr7Mo2B10 alloy. Hc of Fe81Zr7Mo2B10 alloy as-quenched and annealed at low temperature is higher than Fe82Zr7B10Cu1 alloy. However, Hc of Fe81Zr7Mo2B10 alloy annealed at high temperature is lower than the Fe82Zr7B10Cu1 alloy.

In addressing the complexity of modeling biorobotic fish, with a focus on a three-joint robotic fish as the research subject, the model is constructed based on dynamics and kinematics. A analysis of the control methods, dynamics, and kinematic characteristics of both fish and robotic fish is conducted. Using SolidWorks, a physical model of the robotic fish is built and then imported into MATLAB to establish a Simscape simulation model.The simulation experiments are conducted to analyze and evaluate the performance of the robotic fish model. The results indicate that the time-varying curves of the three joint angles in the robotic fish model are relatively smooth without significant mutations or oscillations, demonstrating stable motion behavior. This validates the rationality of the model in terms of design and parameter adjustment.

Due to the volatility of wind energy tends to lead to training sample diversity and the lack of historical data, both of which greatly impact wind power prediction. In response to this problem,  we propose an ultra-short-term wind power prediction method for wind power clusters based on weather feature selection and spatial density clustering. First, a feature selection method based on complete kernel fisher discrimination is used to subject the NWP(Numerical Weather Prediction) information to principal component analysis and extract the most critical wind speed features of each wind turbine. After that, an improved clustering method based on spatial density is used to classify the clusters of each wind turbine in the wind farm based on the above features. Finally, the GRU-D(Gated Recurrent Unit with Decay) method is used to predict the power of each wind turbine cluster and sum it to get the predicted power. The results using historical data of onshore wind power output in a region of Spain show that the root-mean-square error prediction accuracy of the proposed method improves by 0.25% and 2.02% compared to the prediction methods based on traditional DBSCAN(Density-Based Spatial Clustering of Applications with Noise) and K-means clustering methods, and the root-mean-square error prediction accuracy of the model improves by 0.82% compared to the GRU(Gated Recurrent Unit) that cannot handle missing values.

In order to solve the problems of YOLOv5 on the problems of too many small targets on metal surface defects and the detection results are easy to be interfered by background, an improved metal surface defect detection algorithm was proposed. By introducing the coordinate attention mechanism in the backbone network, the model pays attention to defects, and some CBS and C3 modules in the backbone network are replaced with GhostNetV2 structure to build a lightweight network to optimize the performance and efficiency of the model. A bidirectional feature fusion network (BiFPN) was used to enhance the neck layer to generate rich representations, deepen the whole network and reuse low-level features. Finally, extensive experimental results show that the accuracy of CGB-YOLO on NEU-DET reaches 75.0% mAP, which is 3.8% higher than that before the improvement. The model has good comprehensive performance in metal surface defect detection.

An intelligent temperature detection method based on multi-sensor fusion was proposed in order to solve the problem that the precision instrument would generate heat when running in special environment, which would cause the temperature to rise and lead to the life of the precision instrument to be shortened and the original parts to be processed incorrectly. The multi-layer feedforward neural network intelligent detection technology optimized by genetic algorithm is applied to the temperature detection of the incubator. The results show that the predicted temperature value obtained by the multi-layer feedforward neural network optimized by genetic algorithm is basically consistent with the change trend of the actual temperature value, and has high generalization ability and robustness.

Central heating system is a complex control system with the characteristics of time delay, nonlinearity and large inertia, and the effect of traditional PID control cannot achieve satisfactory results, and it also causes a certain waste of resources. Although the BP neural network PID controller improves the performance of the PID controller to a certain extent, the BP neural network itself still has some shortcomings. In order to improve the stability of the heating system and realize the rational use of heat, the particle swarm algorithm (PSO) is used to optimize the weights of the BP neural network PID controller. After designing the PSO?BP?PID controller, the simulation curve of the traditional PID control, BP?PID control and PSO?BP?PID controller is obtained by using MATLAB, and the improvement effect of system performance is obtained according to the comparison of curve effects.

Aiming at the problem of low accuracy of object grasping position detection in robotic arm grasping detection, a 3D point cloud-based robotic arm grasping position detection method is proposed. Firstly, we design an end-to-end grasping position detection network SE-PointNetGPD (SEPN-GPD for short) based on the attention mechanism, and secondly, to address the problem of redundancy of information in the pointnet network when utilizing the multilayer perceptron MLP with shared weights to process the 3D point cloud data, we introduce the SENet module of the channel attention mechanism, and adaptively adjust the weights of the individual feature regions to improve the feature extraction capability of the network and thus improve the accuracy of the grasping position detection method. The SENet module is introduced to enhance the feature extraction capability of the network by adaptively adjusting the weights of each feature region to improve the accuracy and reliability of grasping position detection, which is then validated on the YCB and BigBIRD public datasets. The experimental results show that the classification accuracies of the SEPN-GPD grasping posture detection method are 86.2% and 85.14%, respectively, and the network has a better model generalization ability and higher robustness and stability, which is better than the current mainstream grasping posture detection methods such as PointNetGPD and GPD.

A kind of trampling piezoelectric energy generating floor is designed to convert the mechanical energy generated by pedestrian trampling into electrical energy, which is collected and then supplied to electrical appliances. First of all, 3D modeling software is used to design the mechanical structure, design the energy harvesting system, and then make a prototype to test the maximum output voltage of the actual piezoelectric floor. The theoretical value is compared with the actual value. When the stampede force is 850N, the maximum output voltage of the piezoelectric floor is 14.6V, and its performance meets the expectations.

Acascade feedforward control scheme was designed to address the disturbance caused by changes in feed rate during methanol pre-distillation process, and a PLC control program was written using STEP7 software. The test results showed that the feedforward cascade control scheme can effectively solve the feed interference problem under traditional control methods, and has certain reference significance for the stable control of actual tower and kettle temperature in production sites.

In order to control the preheating temperature of aromatic extraction column, a compound control strategy combining feedforward and feedback was adopted. The mathematical model of the controlled object was obtained by system identification of the production data, and the simulation test of the mathematical model was completed by Simulink software.Based on Siemens S7-300 series PLC, the PLC program of compound control scheme was written by STEP7 software, which provides reference for practical engineering application.

This paper uses the self-decoupling control (SDC) method to study the liquid level control of a three-tank system. Firstly, a linear extended state observer is employed to estimate and compensate for the coupling, non-linear elements, and disturbances in the three-tank system. Subsequently, by designing appropriate control laws, the self-decoupling of each subsystem of the system is achieved. Simulation results indicate that compared with traditional active disturbance rejection control, the self-decoupling control method demonstrates great dynamic tracking performance for the desired liquid level values with faster response speed and better robustness.

Taking the HVAC with time delay characteristics as the controlled object, the Smith predictive control algorithm and the adaptive algorithm are combined, and the adaptive law is derived based on Lyapunov's stability theory, and the Smith adaptive identifier is designed, so as to realize the self-tuning of controller parameters, and solve the problem of parameter time-varying in the control process, and the control effect is optimized. Experimental simulation results show that the Smith adaptive identifier control algorithm is better than the conventional Smith predictive control algorithm.

The data within various departments of universities are intertwined and difficult to interconnect with each other, making it challenging to achieve data sharing and utilization. As a result, it leads to the problem of data silos, where the full value of data cannot be realized. To address the aforementioned issues, universities have been constructing data governance for smart campus by establishing data service platforms. This article proposes feasible solutions to tackle problems such as the lack of top-level coordination and planning, poor data quality, and insufficient data service capabilities. These solutions aim to provide more possibilities for the development of data governance in building smart campuses.The data within various departments of universities are intertwined and difficult to interconnect with each other, making it challenging to achieve data sharing and utilization. As a result, it leads to the problem of data silos, where the full value of data cannot be realized. To address the aforementioned issues, universities have been constructing data governance for smart campus by establishing data service platforms. This article proposes feasible initial solutions to tackle problems such as the lack of top-level coordination and planning, poor data quality, and insufficient data service capabilities. These solutions aim to provide more possibilities for the development of data governance in building smart campuses.

Real-time monitoring of coal mines underground, collecting image information, and conducting safety alerts are important aspects of mine safety research. However, factors such as lighting and dust affect the clarity of images collected underground in coal mines. To address this issue, an image enhancement algorithm for coal mine underground images based on the HSV space Retinex has been proposed. This algorithm maps RGB images to HSV space, processes the corresponding components in HSV space, and then remaps them back to RGB space. In experiments, both images reflecting the overall information of the mine and images showing local details of objects in the mine were enhanced. The results show that compared to the traditional Retinex algorithm, the images processed by this algorithm have improved in terms of SSIM, PSRN, and information entropy, and are more effective in enhancing the edge details of the images, which is of great significance for the safety precautions in coal mines underground.