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Based on a Western perspective, this study utilized big data technology and web crawlers to obtain relevant ethnic and cultural information resources in Japan. Subsequently, these resources were sorted out, including the standardization and classification of content, to form a cultural resource base with national characteristics. At the same time, a BP neural network was used to evaluate the relevant factors. Finally, the accuracy and reliability of the model were verified by collecting relevant data and applying multiple linear regression methods. Among the positive probabilities, personal thoughts and social atmosphere were 0.8 and 0.66, respectively, which influenced the formation and orientation of the Japanese national cognitive structure to a certain extent. Traditional customs and religious beliefs are the factors that contribute the most to ethnicity cognition, with weights of 0.5474 and 0.4689, respectively. In the significance analysis, the p-values of dietary culture, dress culture and geographic environment are greater than 0.05, which indicates that these factors do not have a significant positive influence on ethnicity cognition.

Rapid climate variability and intense human activities generate obvious impacts on the Qilian Mountains ecosystem. The time series of fractional vegetation coverage (FVC) from 1986 to 2021 were used to quantify the impact of climate variability and human activities on vegetation variations in the Qilian Mountain National Nature Reserve (QMNNR), using 3147 land satellite images based on the Google Earth Engine cloud platform. The contributions of climate variability and human activities to FVC were quantified using multiple regression residual analysis. Partial correlation and correlation methods were used to quantify the impact of temperature, precipitation, and human activity footprints on FVC. The results showed that from 1986 to 2021, the increase rate of FVC was 1.7 × 10−3 y−1, and the high vegetation coverage of the FVC was mainly distributed in the southeastern part of the reserve. In contrast, the low vegetation coverage was mainly distributed in the northwest part of the reserve. The Mann–Kendall mutation test found that the year of 2009 was the year of the mutation. The growth rate of FVC from 2010 to 2021 was greater than that from 1986 to 2009. In addition, climate variability and human activities exhibited a remarkable spatial heterogeneity in FVC changes. Climate variability and human activities contributed 49% and 51% to the increase in FVC in the reserve, respectively, and the contribution of human activities was greater than that of climate variability. The warming and humidification phenomena in the reserve were obvious. However, precipitation was the dominant factor affecting the dynamic changes in FVC. This study improves our understanding of the response of vegetation dynamics to the climate and human activities in the QMNNR. [ABSTRACT FROM AUTHOR]

The reliability of liquid rocket engines (LREs), which are the main propulsion device of launch vehicles, cannot be overemphasised. The development of fault detection and diagnosis (FDD) technology for LREs can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. With the rapid development of artificial intelligence technologies such as machine learning and artificial neural network, data-driven FDD methods have gained increasing attention. However, the scarcity of engine fault samples limits the application of this methods. We proposed a method combining Wasserstein generative adversarial nets (WGANs) and multilayer perceptron (MLP) to perform FDD for LREs with sample imbalance. Wasserstein generative adversarial nets were trained using the fault data from the actual hot-firing ground test of a large LRE. Considerable fault data were generated to expand the data set to balance the ratio of positive and negative samples. Subsequently, the expanded data set was used to train the MLP for FDD of a large LRE. The results showed that the samples generated by the WGAN were authentic, confirming the application of the proposed method as a novel and effective tool for establishing a complete LRE fault database. Furthermore, the diagnosis times of the proposed method on five fault tests were advanced by 0.66, 15.82, 0.24, 0.14 and 1.08 s in relation to those of the conventional red-line cut-off system. Compared with support vector machine and adaptive threshold algorithm, the proposed method also performed better. [ABSTRACT FROM AUTHOR]

Etlingera velutina belongs to the Zingiberaceae family. The plant is known as a potent agent for snakebite and food flavoring in the east of Asia. In this study, the essential oil from the rhizomes of E. velutina was obtained using the hydro-distillation assay. The chemical composition of the oil was identified by chromatographic methods of GC/FID and GC/MS. The MTT assay was run to evaluate the cytotoxicity effect of the essential oil. The oil consists of sesquiterpene hydrocarbon. α-Cubebene (57.4%) and 2-undecanone (28.6%) were the major compounds for the rhizome oil. The MTT assay was run to treat the cell lines with essential oil for evaluation of the cytotoxicity and anti-human colorectal carcinoma properties. The IC50 of essential oil was 241 and 214 μg/mL against ileocecal colorectal adenocarcinoma (HCT-8 [HRT-18]) and Burkitt's lymphoma (Ramos.2G6.4C10) cell lines, respectively. The results showed a dose-dependently on The viability of malignant colorectal cell lines in the presence of essential oil. The obtained results revealed the essential oil of the rhizomes of E. velutina is a potent agent against colorectal carcinoma. [ABSTRACT FROM AUTHOR]

The development of health monitoring technology for liquid rocket engines (LREs) can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. Therefore, we propose a fault detection and diagnosis (FDD) method for a large LOX/kerosene rocket engine based on long short-term memory (LSTM) and generative adversarial networks (GANs). Specifically, we first modeled a large LOX/kerosene rocket engine using MATLAB/Simulink and simulated the engine's normal and fault operation states involving various startup and steady-state stages utilizing fault injection. Second, we created an LSTM-GAN model trained with normal operating data using LSTM as the generator and a multilayer perceptron (MLP) as the discriminator. Third, the test data were input into the discriminator to obtain the discrimination results and realize fault detection. Finally, the test data were input into the generator to obtain the predicted samples and calculate the absolute error between the predicted and the real value of each parameter. Then the fault diagnosis index, standardized absolute error (SAE), was constructed. SAE was analyzed to realize fault diagnosis. The simulated results highlight that the proposed method effectively detects faults in the startup and steady-state processes, and diagnoses the faults in the steady-state process without missing an alarm or being affected by false alarms. Compared with the conventional redline cut-off system (RCS), adaptive threshold algorithm (ATA), and support vector machine (SVM), the fault detection process of LSTM-GAN is more concise and more timely. [ABSTRACT FROM AUTHOR]

Ecological compensation, which is known as an effective pathway to restore ecosystems, has been widely adopted in trans-boundary river basins management. Public participation is an important determinant of the efficiency and efficacy of an eco-compensation program. However, how to motivate public participation in eco-compensation in trans-boundary basins is still a challenge. Public intention and motivation regarding ecological compensation are the determinants of public participation in ecological compensation. In this study, the theory of planned behaviour is extended to explain the motivations for public eco-compensation intention in a trans-boundary river basin. Public eco-compensation intention is measured by the contingent valuation method. The main findings show that (1) the mean willingness to pay (WTP) in the downstream area and the mean willingness to accept payment (WTA) in the upstream area are 51.08 RMB and 373.9 RMB per month per household, respectively. WTA/WTP divergence is approximately 7.32 in this study. (2) Upstream of the Taipu River Basin, behavioural attitude, subjective norms, knowledge of eco-compensation and satisfaction with government work are significant influential factors of residents' behavioural intention towards eco-compensation. In the downstream area, behavioural attitude, perceived behavioural control and risk perception are among the most significant influential factors. (3) Situation awareness can promote residents' subjective norms and perceived behavioural control in downstream areas. A pathway to promote public participation in eco-compensation is given based on residents' eco-compensation intention and motivation. • CVM and TPB model were used to measure residents' eco-compensation intention in a trans-boundary river basin. • RP, SGW, KE were extended to TPB model to explain the residents' eco-compensation intention in transboundary river basins. • BA, SN, KE, and SGW influence residents' eco-compensation intention in the upper reach of the Taipu river. • BA, PBC, and RP influence residents' eco-compensation intention in the lower reach of the Taipu river. [ABSTRACT FROM AUTHOR]