Polyacrylamide hydrogel is kind of typical hydrogel, which is characterized by three-dimensional structure and high chemical stability. Because of the positive amino group in the structure, the surface of polyacrylamide hydrogel is positive. Considering the porous structure of acrylamide hydrogel, it can adsorb and remove inorganic and organic pollutants in water through the combination of capillary attraction and electrical attraction. Therefore, polyacrylamide hydrogel has the potential to be used for sewage purification. At this point, the mechanical properties of the gel become extremely important. Based on this, this paper focuses on the interaction between the amount of acrylamide as well as the agent and the mechanical properties of hydrogel.

Hydrogen energy, as a kind of high energy density, clean and efficient secondary energy, is considered as an ideal green energy. In hydrogen energy application, large-scale hydrogen production, storage and transportation can significantly reduce the energy consumption and cost of hydrogen market application. The energy consumption and energy efficiency of high-pressure hydrogen and liquid hydrogen storage technologies in sea and land transportation are investigated. The results show that high-pressure hydrogen has certain advantages in short distance transportation, and its energy consumption and cost increase with the increase of transportation distance. When hydrogen is stored and transported at 20MPa, its energy consumption and cost increase obviously with the increase of transportation distance. Hydrogen liquefaction requires high energy consumption but low energy efficiency, and the evaporation loss of hydrogen is accoMPanied by the whole process of liquefaction. Therefore, it is not suitable for cross-sea and long-distance transportation.

The chemical additives of ABS resin have a decisive influence on the specific physical and chemical properties of the finished product. Through the analysis of the components of ABS resin system and the principle of emulsion polymerization, the emulsifiers such as disproportionated rosin potassium soap and sodium alkyl naphthalene sulfonate, as well as the addition of cumene hydroperoxide, dimethyl silicone oil, magnesium stearate, tert-dodecyl mercaptan, N, N-ethylene bis stearamide and other chemicals and the doping of ABS powder were tested to clarify the effect of additives on their physical and chemical properties, and provide a theoretical basis for the optimization of production process.

A 'hopper scale' for packaging systems with quantitative weighing capabilities. The box body of the scale is fixed on the foundation by several lifting lugs hanging weighing sensors. The feeding door of the box body is controlled by the PLC output to control the cylinder action. The feeding door has settings for coarse feeding and fine feeding to ensure weighing accuracy. Because its design is too complicated and cumbersome, there are many detection components, and the failure rate is high, it is often stopped due to the problem of 'glue blocking' caused by a failure in a certain link. It is time-consuming and laborious to clean the rubber in the scale, and the waste rubber produced will cause great losses. Therefore, it is considered to transform the feed hopper into a 'split' structure, which can be separated from the left and right sides. Through the above improvements, it can achieve fast processing, reduce downtime and maintenance time, and reduce production costs.

Traditional bibliometric methods tend to ignore the word frequency of the text in the literature, which leads to the analysis results that can't better highlight the core topics and research hotspots in related fields. This paper proposes a bibliometric method based on the combination of text mining and knowledge map. Through co-word analysis, the subject word matrix is constructed, and the core themes and research hotspots of the literature are analyzed by co-occurrence analysis, text clustering, data visualization and bibliometrics, so as to provide reference for subsequent scholars' research. Taking the literature of higher education reform as an example, the research shows that the bibliometric method combining text mining and knowledge map can obtain the core themes and research hotspots in the field of higher education reform and provide data support for the follow-up research of higher education reform.

In recent years, with the rapid development of big data mining technology in the medical industry, clinical precision therapy has become a research hotspot in the field of medical big data. In this study, based on the breast cancer dataset in the UCI database, a breast cancer dichotomous classification algorithm was constructed to predict breast tumour types. Among them, machine learning techniques including random oversampling algorithm, Least absolute shrinkage and selection operator (Lasso) regression for feature selection, and sequential forward selection (SFS) for feature selection algorithm were used for the processing of imbalanced dataset, optimisation of feature selection algorithm and evaluation of classification accuracy. The results showed that the random forest algorithm containing six of these features had the highest classification accuracy (97.07%), which improved the accuracy relative to the algorithm without feature selection and could potentially provide new ideas in breast cancer detection.

With the increasing concern over environment protection, economic and environmental power dispatch has become one of the active research areas in the power system operation and control. It is essentially a Multi-Objective Optimization Problem, which minimizes both fuel cost and emission pollution simultaneously without violating the operational constraints. To solve this problem, this article presents a new hybrid algorithm based on kernel search optimization (KSO) and sparrow search algorithm (SSA) (HKSOSSA). The proposed HKSOSSA optimization algorithm is employed to solve IEEE 6-unit EED problem. The results show that the proposed HHKSOSSA algorithm for EED problem can obtain a better trade-off between fuel cost and emission objectives compared to those reported algorithms in the recent literature.

Aiming at the problem that it is difficult to effectively extract fault features and accurately identify faults when wind turbines fault, In this paper, a wind turbine fault diagnosis method based on improved SE-CNN is proposed. Firstly, a sliding window is used to expand the historical operating data of the wind turbine in fault collected by the supervisory control and data acquisition (SCADA) system, secondly, the improved squeezed excitation network (SEnet) is used to adjust the weights of the sample data, the global maximum pooling layer is used to improve the convolutional neural network (CNN), and finally the improved CNN is used to learn the fault features and perform fault diagnosis. The experimental results show that the improved SE-CNN outperforms RNN, PCA-DNN, and BiLSTM methods in fault diagnosis, which verifies the effectiveness of the proposed method in wind turbine fault diagnosis.

Multi-effect evaporation is one of the more widely used evaporation methods in the chemical production process. Due to the multi-effect evaporation equipment investment, energy losses, operating costs, etc. accounted for a large proportion of production costs; the multi-effect evaporation process has a wide range of parameters, which need to be approximated in order to better optimise the design. Therefore, a mathematical model of the multi-effect evaporation process is developed and solved using the principles of energy conservation and material conservation in this paper. The results show that the mathematical model established by the method meets the design requirements and provides a certain reference value for model research and solution of similar process systems.

Aiming at the problems of large overshoot, slow response and long stabilization time for traditional heating system PID control, a genetic algorithm was proposed.Combining genetic algorithm with PID，a GA-PID controller was designed to make the system response time short and stable.

Inverted pendulum is a typical underactuation system with multi-variable, nonlinear and strong coupling. Aiming at the control problem of first order inverted pendulum, the mathematical model of inverted pendulum is analyzed and established by Newton-Euler method.The LQR controller was designed based on linear state feedback, and the influence of the weight coefficient of the LQR controller on the control performance was studied. The simulation model was built based on Simscape environment. The experimental results show that the LQR control is stable, the reaction time is fast, and the robustness is strong.

Aiming at the problems of poor stability of conventional PID control algorithm in the temperature control system of morpholine refining tower top, it is not easy to stabilize after being disturbed, the integral separation control algorithm is introduced, Based on Siemens S7 series PLC, the integral separation PID control block program is compiled in the STEP7 programming software, and applied to the temperature control system of the top of the Morpholine refining tower. The result shows that the integral separation PID algorithm effectively reduces the over regulation oscillation amplitude of the system, improves the stability of the control system, and achieves good results in production.

Aiming at the large hysteresis characteristic of ammonia vaporization process of a synthetic ammonia process liquid, the basic principle and applicability of Dahlin control algorithm are studied. The mathematical model of the controlled object was obtained by identifying the field production data, use Siemens S7 series PLC control platform,the control program of liquid ammonia vaporization temperature based on Dahlin algorithm is developed, and good control effect is obtained in the simulation experiment, it has good reference significance for engineering practical application.

To study the results of radical surgery with or without adjuvant radiotherapy for primary squamous cell carcinoma of the breast. Methods:Data of patients were obtained from the SEER database, which was diagnosed histologically primary squamous cell carcinoma of the breast. The competing hazards model, Kaplan-Meier method and Lasso Cox regression model were used to evaluate the effect of surgical type with or without adjuvant radiotherapy on cause-specific survival and overall survival.Results:A total of 454 patients with primary breast squamous cell carcinoma were included in this study. The multivariate competitive risk model analysis showed that patients receiving mastectomy alone had a higher risk of dying of cancer than patients receiving tumor resection plus adjuvant radiotherapy(HR 1.77, p<0.05). According to Kaplan Meier analysis, the survival rate of patients receiving tumor resection plus adjuvant radiotherapy is higher than that of patients receiving tumor resection, mastectomy and mastectomy plus adjuvant radiotherapy. Conclusion: Tumor resection plus adjuvant radiotherapy significantly improves the survival rate, which may be a treatment option for primary breast squamous cell carcinoma.

A dot matrix character segmentation method based on machine vision was proposed to address the low recognition rate of dot matrix characters caused by complex backgrounds, local leakage, or blurred characters. First, the rectangular area where the dot matrix characters were located was quickly located from the image using Blob analysis and rotated to the horizontal direction. Then, the rectangular area was segmented into two rows using horizontal grey scale projection, and the characters in each row were segmented using vertical grey scale projection combined with character width features. Finally, the segmented characters were fed into the MLP classifier for recognition. The experimental results show that the segmentation accuracy of the method is 98.7%, which is significantly better than existing segmentation methods, and also has strong recognition generality; therefore, the method has good application value in the recognition of dot matrix characters.

In order to improve the dust removal efficiency of the spray dust removal system, a variable particle size spray dust removal control system based on the improved BP neural network PID is designed. The system takes spray air pressure as the control variable, establishes the relationship function between spray pressure and spray particle size, optimizes the parameters of the spray pressure PID controller of the variable particle size spray dust removal system by using the BP neural network algorithm, and uses Simulink to simulate the variable particle size spray dust removal system using the traditional PID controller and the improved BP neural network PID controller respectively. Experimental results show that the improved BP neural network PID algorithm improves the response speed by 66.63% and the stability by 20% compared with the traditional PID algorithm, which greatly improves the spray pressure control effect of the variable particle size spray dust removal system.

In this paper, the six degrees of freedom mechanical arm is produced by Hyundai Corporation of Korea. The model is established by improved D-H method, and the geometric parameters of the robot are obtained, finally, an effective forward and inverse motion algorithm is proposed. The forward and inverse kinematics can be accurately predicted by simulation with MATLAB software, and the trajectory planning of the manipulator in the joint space is described in detail, finally, the workspace of the manipulator is Monte Carlo method.

In this paper, the risk of system failure was obtained through Bayesian probabilistic risk assessment method for aircraft power system. Bayesian models of (Ga-Exp) and (D-M) as to common cause failure group were established in order to obtain the Bayesian estimation of the failure rates of the five types of components and the common cause failure ratio of each order. According to the Alpha factor model, the failure rate of the components in the common cause failure group was estimated. Finally, the Bayesian risk probability of system failure was studied. The sensitivity of component failure to system failure risk was defined as component importance index. By a practical example, with the cumulative flight time, the risk of system failure, the importance of common cause failure group components and the five types of components were compared in the two cases of whether considering common cause failure. The validity of the model and the necessity of considering common cause failure factors were verified so as to provide the basis for maintaining the reliability of aircraft.

In this paper, the existence, the continuity, differentiability and integrability of parametric integrals corresponding to uniformly convergent limit functions and the series of function items are studied through the existence, the continuity, differentiability and integrability of parametric integrals of binary function sequences. We obtain the conditions of the exchange order of parametric integrals about limit function and the limit order, derivative and the cumulative integral in the sence of uniformly convergence. At last, the conditions that exchange order of parametric integrals and infinite sum and derivative are obtained.

In recent years, with the internal and external operations such as policy supervision, power reform, macroeconomic and big data technology development, etc.With the change of environment, the business model and profit model of power grid enterprises are deepening, and the demand for investment is more diversified and complicated. Power grid plays an important role in promoting carbon emission reduction in the whole power industry, and it is the hub connecting power plants and users. With the future environmental objectives becoming the main objectives of electric power enterprises, electric power enterprises need to evaluate and analyze various factors in their investment activities. In this paper, several indicators affecting the development of power grid are selected from three aspects: social performance, economic performance and environmental performance. Based on the big data of electric power, the correlation degree between each indicator and power grid investment is obtained by using the grey correlation analysis method, and finally the key indicators affecting power grid investment are obtained.