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Under atmospheric pressure, KOH or HCl solution was added to the butylated hydroxytoluene (BHT) oxidation system to adjust the pH, and the oxidative consumption of BHT was monitored by high performance liquid chromatography (HPLC). To investigate the oxidation properties of BHT under pressurized conditions, the temperature and pressure changes during the oxidation process were monitored using the mini-closed pressure vessel test (MCPVT). The oxidation products of BHT were characterized after crystallization and purification and the possible oxidation pathway was speculated. The results showed that under atmospheric pressure, the degradation rate of BHT was less than 5% when the pH was 4 or 6, indicating good stability. At pH 10, the degradation rate of BHT was up to 59% and the oxidation reaction was rapid. KOH and HCl decreased the initial oxidation temperature of BHT from 133.1 ℃ to 53.0 ℃ and 49.1 ℃ in a pressurized closed container, respectively. 2, 6-Di-tert-butyl-4-hydroperoxy-4-methyl-2, 5-cyclohexanedione and 2, 6-di-tert-butyl-4-methyl-4-(3, 5-di-tert-butyl-4-hydroxyphenyl)-2, 5-cyclohexadien-1-one were produced during the oxidation of BHT in both atmospheric alkaline and pressurized environments. The experimental results suggested that BHT was susceptible to oxidative degradation in alkaline and pressurized environments, producing toxic products, posing safety risks.

Aiming at the characteristics of large solution space, discrete, dynamic and nonlinear of firepower-target assignment problem, this paper proposes a deep reinforcement learning algorithm based on DQN. By combining the 6-layer fully connected feedforward neural network with the Q-learning algorithm, the perception ability of deep learning and the decision-making ability of reinforcement learning are fully utilized. Through the comparison of model performance tests, this method has strong fitting ability, fast convergence speed and small variance jitter, and the distribution results meet the combat expectations, which can provide some reference for commanders to make decisions on fire strike problems.

Research objectives: specify the framework of using cutting-edge approaches and innovations in the training of rehabilitation physicians; investigate existing innovative technologies; get an insight into the motivation of students majoring in rehabilitation when working with the Gross simulator in sports rehabilitation of patients. The research was conducted at a mixed-type rehabilitation center for disabled adults and children at Soochow University in China. The study was conducted among 200 students majoring in rehabilitation at Soochow University and 100 children from a rehabilitation center with motor disorders: 50 boys and 50 girls. The patients were aged 4-6 years, and the average age of the participating students was 21 years. The students were offered a 6-month training course in rehabilitation of patients with musculoskeletal disorders, by using innovative technology--the Gross simulator. Student motivation analysis yielded the following outcomes: 90% of the students said that training to operate simulators is useful for the rehabilitation specialist, because this affects the proficiency of such specialist; 15% of students reported they did not like innovative technology in teaching because they faced difficulties in understanding it; 80% of students believed such technology facilitated the learning, and 5% of students were neutral. Further studies, conducted on clinical research sites, might address the effectiveness of training rehabilitation specialists, the use of cutting-edge simulators and robotic systems, as well as development of rehabilitation protocols for patients with injuries and musculoskeletal disorders.

A field is described as a region under the influence of some physical force, such as electricity, magnetism, or heat. It is a continuous distribution in the space of continuous quantities. The characteristics of the field are that the values vary continuously between neighboring points. However, because of the continuous nature of the field, it is possible to approximate a physical field of interpolation operations to reduce the cost of sampling and simplify the calculation. This article introduces the modeling of the parametric intensity of physical fields in a general form based on the interpolation shape function technique. Besides the node points with sample data, there are interpolation points, whose accuracy depends significantly on the type of interpolation function and the number of node points sampled. Therefore, a comparative analysis of theoretical shape functions (TSFs) and experimental shape functions (ESFs) is carried out to choose a more suitable type of shape function when interpolating. Specifically, the temperature field is the quantity selected to apply, analyze, and conduct experiments. Theoretical computations, experiments, and comparisons of results have been obtained for each type of shape function in the same physical model under the same experimental conditions. The results show that ESF has an accuracy (error of 0.66%) much better than TSF (error of 10.34%). Moreover, the field model surveyed by a generalized reduced gradient algorithm allows for identifying points with the required parameter values presented in detail. The illustrated calculations on temperature field control in the article show that the solution for both forward and reverse problems can be determined very quickly with high accuracy and stability. Therefore, this technique is expected to be entirely feasible when applied to thermal control processes such as drying in paint technology, kilns, and heat dissipation in practice.