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With the development of aircraft design toward more electricity,the development of electric environmental control system in more electric aircraft has received wide attention.This paper introduces the operation principle of Boeing B787 electric environmental control system,and reviews the current research status on the architectural design and trade-offs of electric environmental control system and the design of key components.It is proposed that since the adoption of electric environmental control system design will aggravate the complexity of the aircraft,integrated tools are needed to support the architectural design of electric environmental control system,and to develop the agent modeling techniques that can replace CFD calculations to shorten the design cycle of key components.This paper will provide a reference for the research and development of domestic electric environmental control systems.

Traditional integer-order admittance control is widely used in industrial scenarios requiring force control, but integer-order models often struggle to accurately depict fractional-order-controlled objects, leading to precision bottlenecks in the field of precision machining. For robotic precision polishing scenarios, to enhance the stability of the control process, we propose a more physically accurate five-parameter fractional-order admittance control model. To reduce contact impact, we introduce a method combining the rear fastest tracking differential with fractional-order admittance control. The optimal parameter identification for the fractional-order system is completed through Pareto optimality and a time–frequency domain fusion analysis of the control system. We completed the optimal parameter identification in a simulation, which is applied to the robotic precision polishing scenario. This method significantly enhanced the force control precision, reducing the error margin from 15% to 5%. [ABSTRACT FROM AUTHOR]

As one of the most critical components of the power grid system, transformer maintenance strategy planning significantly influences the safe, economical, and sustainable operation of the power system. Periodic imperfect maintenance strategies have become a research focus in preventive maintenance strategies for large power equipment due to their ease of implementation and better alignment with engineering realities. However, power transformers are characterized by long lifespans, high reliability, and limited defect samples. Existing maintenance methods have not accounted for the dynamic changes in maintenance costs over a transformer's operational lifetime. Therefore, we propose a maintenance interval optimization method that considers imperfect maintenance and dynamic maintenance costs. Utilizing defect and maintenance cost data from 400 220 KV oil-immersed transformers in northern China, we employed Bayesian estimation for the first time to address the distribution fitting of defect data under small sample conditions. Subsequently, we introduced imperfect maintenance improvement factors to influence the number of defects occurring in each maintenance cycle, resulting in more realistic maintenance cost estimations. Finally, we established an optimization model for transformer maintenance cycles, aiming to minimize maintenance costs throughout the transformer's entire lifespan while maintaining reliability constraints. Taking a transformer's strong oil circulation cooling system as an example, our method demonstrates that while meeting the reliability threshold recognized by the power grid company, the system's maintenance cost can be reduced by 41.443% over the transformer's entire life cycle. Through parameter analysis of the optimization model, we conclude that as the maintenance cycle increases, the factors dominating maintenance costs shift from corrective maintenance to preventive maintenance. [ABSTRACT FROM AUTHOR]

Sunspots have a significant impact on human activities. In this study, we aimed to improve solar activity prediction accuracy. To predict the sunspot number based on different aspects, such as extracted features and relationships among data, we developed a hybrid model that includes a one-dimensional convolutional neural network (1D-CNN) for extracting the features of sunspots and bidirectional long short-term memory (BiLSTM) embedded with a multi-head attention mechanism (MHAM) to learn the inner relationships among data and finally predict the sunspot number. We evaluated our model and several existing models according to different evaluation indicators, such as mean absolute error (MAE) and root mean square error (RMSE). Compared with the informer, stacked LSTM, XGBoost-DL, and EMD-LSTM-AM models, the RMSE and MAE of our results were more than 42.5% and 65.1% lower, respectively. The experimental results demonstrate that our model has higher accuracy than other methods. [ABSTRACT FROM AUTHOR]

ObjectiveTo analyze the mental health status and the influencing factors among the residents taking standardized residency training during the outbreak and stable period of COVID-19， so as to provide theoretical basis for their mental health education.MethodsOn February 8 to February 10 and April 11 to April 13， 2020， Symptom Checklist 90 （SCL-90）， Beck Srivastava Stress Inventory （BSSI） and Simplified Coping Style Questionnaire （SCSQ） were distributed in online chat groups involving all grades of residents taking standardized residency training in the Second Affiliated Hospital of Kunming Medical University. Through two rounds of questionnaire survey， non-probability sampling method was used to obtain survey samples， and their mental status were analyzed.ResultsA total of 159 valid questionnaires were collected in the first round， and 99 valid questionnaires in the second round. The first survey showed that the total score of SCL-90 was （117.69±37.74） and the detection rate of positive symptoms in SCL-90 was 25.8%， and mainly dominated by obsession， fear and interpersonal sensitivity. In the second survey， the results showed that the total score of SCL-90 was （127.19±51.44）， and the main positive symptoms included obsession， depression and interpersonal sensitivity， with a positive detection rate of 30.3 %. The first survey found that the mental health status had significant differences among residents of different grades （χ2=7.46， P

In the context of predicting the remaining useful life (RUL) of rolling bearings, many models often encounter challenges in identifying the starting point of the degradation stage, and the accuracy of predictions is not high. Accordingly, this paper proposes a technique that utilizes particle swarm optimization (PSO) in combination with the fusing of a one-dimensional convolutional neural network (CNN) and a multihead self-attention (MHSA) bidirectional long short-term memory (BiLSTM) network called PSO-CNN-BiLSTM-MHSA. Initially, the original signals undergo correlation signal processing to calculate the features, such as standard deviation, variance, and kurtosis, to help identify the beginning location of the rolling bearing degradation stage. A new dataset is constructed with similar degradation trend features. Subsequently, the particle swarm optimization (PSO) algorithm is employed to find the optimal values of important hyperparameters in the model. Then, a convolutional neural network (CNN) is utilized to extract the deterioration features of rolling bearings in order to predict their remaining lifespan. The degradation features are inputted into the BiLSTM-MHSA network to facilitate the learning process and estimate the remaining lifespan of rolling bearings. Finally, the degradation features are converted to the remaining usable life (RUL) via the fully connected layer. The XJTU-SY rolling bearing accelerated life experimental dataset was used to verify the effectiveness of the proposed method by k-fold cross-validation. After comparing our model to the CNN-LSTM network model and other models, we found that our model can achieve reductions in mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 9.27%, 6.76%, and 2.35%, respectively. Therefore, the experimental results demonstrate the model's accuracy in forecasting remaining lifetime and support its ability to forecast breakdowns. [ABSTRACT FROM AUTHOR]