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Effective data cleaning methods can improve the quality of wind turbine measurement data. The quality of wind turbine data plays a very important role in wind resource assessment, wind power accurate prediction, and performance diagnosis of wind turbines. There are many uncertainties in the data collection and monitoring systems of different wind turbines for fault diagnosis, which result in uneven quality of wind measurement data for wind turbines. This paper proposes a new method for identifying the probability interval of wind power. This method uses the characteristic changes between wind speed and power to clean and correct the effective data of wind turbine measurement data. It can effectively improve the utilisation rate of wind turbine data. This paper selects wind turbine data from a wind farm in the northern part of Ulanqab, Inner Mongolia Autonomous Region from 2020 to 2022. By sequentially subjecting the data to rationality and validity tests, wind power interval checks, and finally, cleaning and correcting abnormal data, which are carried out by utilising the correlation of the turbine. The final results indicate that: by using the wind power interval method, it is difficult to distinguish abnormal wind speeds. This method can improve data quality and enhance the accuracy of wind speed and power. According to statistics, the data integrity has been significantly improved from 68.7%-92.5% to 90.1%-92.7%. Above all, the data integrity has been significantly improved. This method achieves mutual calibration between wind speed and power through the wind power probability interval recognition method. It provides fundamental support data for predicting and regulating the power generation of wind farms. It provides guidance and a basis for more refined meteorological service products for power and other related sectors.

Although desert ecosystems are commonly considered to have low carbon storage owing to their sparse vegetation and limited carbon sequestration capacity, they comprise 13.5% of China's terrestrial ecosystems and cover approximately 35% of the Earth's land surface, so they still possess significant carbon storage. The carbon storage of desert ecosystems in China was estimated using MODIS-NDVI data in combination with ground survey, soil census, and literature statistical data. The results indicated that the carbon storage of desert ecosystems in China was 7.063 Pg in 2020. One-meter soil carbon storage accounted for the largest proportion (89.514%), approximately 6.322 Pg, followed by vegetation carbon at 0.741 Pg, and biological soil crust carbon at 0.002 Pg. The carbon density of desert ecosystems was 33.895 Mg.ha−1, with soil carbon density, vegetation carbon density, and biological soil crust carbon density at 30.330, 3.554, and 0.011 Mg.ha−1, respectively. This research provides important evidence for the correct understanding of the carbon storage of desert ecosystems in China.

PTFE's exceptional low-temperature resistance makes it an ideal material for cryogenic aerospace fuel seals. However, the current understanding of its mechanical behaviors at low temperatures remains limited, posing potential design vulnerabilities. This study investigates the quasi-static mechanical properties of PTFE over a temperature range from 77 K to room temperature, with a particular focus on the effects of temperature and strain rate, which are key variables in sealing applications. The experimental conditions are categorized into ambient temperature (293 K), conventional low temperatures (233 K–293 K), and cryogenic temperatures (77 K–223 K). Testing was conducted at crosshead speeds of 1, 10, and 100 mm/min to evaluate their influence. The results reveal significant phase transition characteristics in PTFE's low-temperature mechanical properties, with the effects of phase transitions far outweighing those of strain rate, leading to dramatic changes in the stress response, from elasticity through plasticity to fracture. Further analysis indicates that these effects are concentrated around the transition state temperature zone, with slower changes observed at other temperatures. This study highlights the critical importance of considering phase transitions in the low-temperature design of PTFE structures. [ABSTRACT FROM AUTHOR]

In high-temperature and high-pressure environments, spring-energized seals (SES) play a crucial role in the petroleum and energy chemical industries. The circumferential springs within SES are responsible for generating sealing forces to maintain their performance. However, the intricate nonlinear behavior of these springs poses significant challenges in modeling and simulation. While several simplified models have been proposed to enhance computational efficiency, they often fall short under conditions of substantial deformation. Consequently, this paper introduces an anisotropic equivalent modeling approach, which takes into account the mechanical anisotropy of the springs. By applying the principles of using the same structural dimensions and equivalent mechanical properties, the parameters of the springs are transformed into equivalent parameters on anisotropic materials. The efficacy of this new model is validated through finite element simulations and lateral compression experiments, where its advantages over existing models in terms of predictive accuracy and computational efficiency are discussed. The results indicate that this novel model significantly improves predictive accuracy under conditions of substantial deformation, accompanied by enhancements in computational efficiency. This approach offers a promising tool for designing and predicting the performance of circumferential springs and SES. [ABSTRACT FROM AUTHOR]

Superpixels, as essential mid-level image representations, have been widely used in computer vision due to their computational efficiency and redundant compression. Compared with traditional superpixel methods, superpixel algorithms based on deep learning frameworks demonstrate significant advantages in segmentation accuracy. However, existing deep learning-based superpixel algorithms suffer from a loss of details due to convolution and upsampling operations in their encoder–decoder structure, which weakens their semantic detection capabilities. To overcome these limitations, we propose a novel superpixel segmentation network based on a multi-attention hybrid network (MAS-Net). MAS-Net is still based on an efficient symmetric encoder–decoder architecture. First, utilizing residual structure based on a parameter-free attention module at the feature encoding stage enhanced the capture of fine-grained features. Second, adoption of a global semantic fusion self-attention module was used at the feature selection stage to reconstruct the feature map. Finally, fusing the channel with the spatial attention mechanism at the feature-decoding stage was undertaken to obtain superpixel segmentation results with enhanced boundary adherence. Experimental results on real-world image datasets demonstrated that the proposed method achieved competitive results in terms of visual quality and metrics, such as ASA and BR-BP, compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This study meticulously investigates landscape alterations within the Mu Us Sandy Land, a critical region for desertification control in China. The research dissects the dynamic characteristics and inter-conversion of landscape elements across eleven distinct periods by employing multi-source remote sensing imagery spanning 1963 to 2020, alongside visual interpretation, random forest classification, and the desertification difference index (DDI). The analysis uncovers significant landscape transformations within the Mu Us Sandy Land over the past six decades, following a precise chronological sequence. A pivotal shift occurred around 1986, characterized by opposing trends within fixed and shifting sandy land. The earlier stage (pre-1986) witnessed a substantial decrease (66.9%) in the fixed sandy land area, accompanied by a corresponding rise (38.7%) in shifting sandy land. Conversely, the later stage (post-1986) era exhibited a remarkable increase (309.7%) in fixed sandy land, alongside a significant decline (78.9%) in shifting sand land coverage. This study identifies two stages of landscape transformation: a pre-1986 phase dominated by the conversion of fixed sandy land to semi-fixed sandy land and a post-1986 reversal toward shifting sand land into fixed sandy land. These sequential transformations have shaped the landscape pattern alterations observed in the Mu Us Sandy Land since 1963. The dramatic landscape improvements observed after 1986 can be primarily attributed to the implementation and continued investment in large-scale ecological restoration projects. This study's findings, which reveal the intricate landscape dynamics and their implications for ecosystem management, provide a scientific foundation for refining and formulating comprehensive strategies to control desertification and manage the Mu Us Sandy Land's unique ecosystem. [ABSTRACT FROM AUTHOR]